Estimates Of Use Research Articles

Disentangling marine plastic impacts in Life Cycle Assessment: Spatially explicit Characterization Factors for ecosystem quality

Inputs of persistent plastic items to marine environments continue to pose a serious and long-term threat to marine fauna and ecosystem health, justifying further interventions on local and global scales. While Life Cycle Assessment (LCA) is frequently used for sustainability evaluations by industries and policymakers, plastic leakage to the environment and its subsequent impacts remains absent from the framework. Incorporating plastic pollution in the assessments requires development of both inventories and impact assessment methods. Here, we propose spatially explicit Characterization Factors (CF) for quantifying the impacts of plastic entanglement on marine megafauna (mammals, birds and reptiles) on a global scale. We utilize Lagrangian particle tracking and a Species Sensitivity Distribution (SSD) model along with species susceptibility records to estimate potential entanglement impacts stemming from lost plastic-based fishing gear. By simulating plastic losses from fishing hotspots within all Exclusive Economic Zones (EEZs) we provide country-specific impact estimates for use in LCA. The impacts were found to be similar across regions, although the median CF associated with Oceania was higher compared to Europe, Africa and Asia. Our findings underscore the presence of susceptible species across the world and the transboundary issue of plastic pollution. We discuss the application of the factors and identify areas of further refinement that can contribute towards a comprehensive assessment of macroplastic pollution in sustainability assessments. Degradation and beaching rates for different types of fishing gear remain a research gap, along with population-level effects on marine taxa beyond surface breathing megafauna. Increasing the coverage of impacts specific to the marine realm in LCA alongside other stressors can facilitate informed decision-making towards more sustainable marine resource management.

Characterizing Freshwater Ecotoxicity of More Than 9000 Chemicals by Combining Different Levels of Available Measured Test Data with In Silico Predictions.

Ecotoxicological impacts of chemicals released into the environment are characterized by combining fate, exposure, and effects. For characterizing effects, species sensitivity distributions (SSDs) estimate toxic pressures of chemicals as the potentially affected fraction of species. Life cycle assessment (LCA) uses SSDs to identify products with lowest ecotoxicological impacts. To reflect ambient concentrations, the Global Life Cycle Impact Assessment Method (GLAM) ecotoxicity task force recently recommended deriving SSDs for LCA based on chronic EC10s (10% effect concentration, for a life-history trait) and using the 20th percentile of an EC10-based SSD as a working point. However, because we lacked measured effect concentrations, impacts of only few chemicals were assessed, underlining data limitations for decision support. The aims of this paper were therefore to derive and validate freshwater SSDs by combining measured effect concentrations with in silico methods. Freshwater effect factors (EFs) and uncertainty estimates for use in GLAM-consistent life cycle impact assessment were then derived by combining three elements: (1) using intraspecies extrapolating effect data to estimate EC10s, (2) using interspecies quantitative structure-activity relationships, or (3) assuming a constant slope of 0.7 to derive SSDs. Species sensitivity distributions, associated EFs, and EF confidence intervals for 9862 chemicals, including data-poor ones, were estimated based on these elements. Intraspecies extrapolations and the fixed slope approach were most often applied. The resulting EFs were consistent with EFs derived from SSD-EC50 models, implying a similar chemical ecotoxicity rank order and method robustness. Our approach is an important step toward considering the potential ecotoxic impacts of chemicals currently neglected in assessment frameworks due to limited test data. Environ Toxicol Chem 2024;43:1914-1927. © 2024 The Author(s). Environmental Toxicology and Chemistry published by Wiley Periodicals LLC on behalf of SETAC.

Derivation of Transcriptomics-Based Points of Departure for 20 Per- or Polyfluoroalkyl Substances Using a Larval Fathead Minnow (Pimephales promelas) Reduced Transcriptome Assay.

Traditional toxicity testing has been unable to keep pace with the introduction of new chemicals into commerce. Consequently, there are limited or no toxicity data for many chemicals to which fish and wildlife may be exposed. Per- and polyfluoroalkyl substances (PFAS) are emblematic of this issue in that ecological hazards of most PFAS remain uncharacterized. The present study employed a high-throughput assay to identify the concentration at which 20 PFAS, with diverse properties, elicited a concerted gene expression response (termed a transcriptomics-based point of departure [tPOD]) in larval fathead minnows (Pimephales promelas; 5-6 days postfertilization) exposed for 24 h. Based on a reduced transcriptome approach that measured whole-body expression of 1832 genes, the median tPOD for the 20 PFAS tested was 10 µM. Longer-chain carboxylic acids (12-13 C-F); an eight-C-F dialcohol, N-alkyl sulfonamide; and telomer sulfonic acid were among the most potent PFAS, eliciting gene expression responses at concentrations <1 µM. With a few exceptions, larval fathead minnow tPODs were concordant with those based on whole-transcriptome response in human cell lines. However, larval fathead minnow tPODs were often greater than those for Daphnia magna exposed to the same PFAS. The tPODs overlapped concentrations at which other sublethal effects have been reported in fish (available for 10 PFAS). Nonetheless, fathead minnow tPODs were orders of magnitude higher than aqueous PFAS concentrations detected in tributaries of the North American Great Lakes, suggesting a substantial margin of safety. Overall, results broadly support the use of a fathead minnow larval transcriptomics assay to derive screening-level potency estimates for use in ecological risk-based prioritization. Environ Toxicol Chem 2024;00:1-16. © 2024 SETAC. This article has been contributed to by U.S. Government employees and their work is in the public domain in the USA.

Using Expert Elicitation to Adjust Published Intervention Effects to Reflect the Local Context.

Local health services make limited use of economic evaluation to inform their decisions on the funding of new health service interventions. One barrier to use is the relevance of published intervention evaluations to the local setting.Expert elicitation methods provide a structured way to consider differences between the evaluation and local settings and to explicitly and transparently adjust published effect estimates for use in local economic evaluations.The pragmatic elicitation methods reported in this article offer a feasible and acceptable approach to adjusting published intervention effects to better reflect the effects expected in the local context. This increases the relevance of economic evaluations for local decision makers.

Differences in Self-Reported and Billed Postpartum Visits Among Medicaid-Insured Individuals

State Medicaid programs have recently implemented several policies to improve access to health care during the postpartum period. Understanding whether these policies are succeeding will require accurate measurement of postpartum visit use over time and across states; however, current estimates of use vary substantially between data sources. To examine disagreement between postpartum visit use reported in the Pregnancy Risk Assessment Monitoring System (PRAMS) and Medicaid claims and assess whether insurance transitions from Medicaid at the time of childbirth to other insurance types after delivery are associated with the degree of disagreement. This cross-sectional study was conducted among individuals in South Carolina after delivery who had completed a PRAMS survey and for whom Medicaid was the payer of their delivery care. PRAMS responses from 2017 to 2020 were linked to inpatient, outpatient, and physician Medicaid claims; survey-weighted logistic regression models were then used to examine the association between postpartum insurance transitions and data source disagreement. Data were analyzed from February through October 2023. Insurance transition type: continuous Medicaid, Medicaid to private insurance, Medicaid to no insurance, and Emergency Medicaid to no insurance. Data source disagreement due to reporting a postpartum visit in PRAMS without a Medicaid claim for a visit or having a Medicaid claim for a visit without reporting a postpartum visit in PRAMS. Among 836 PRAMS respondents enrolled in Medicaid at delivery (663 aged 20-34 years [82.9%]), a mean of 85.7% (95% CI, 82.1%-88.7%) reported a postpartum visit in PRAMS and a mean of 61.6% (95% CI, 56.9%-66.0%) had a Medicaid claim for a postpartum visit. Overall, 253 respondents (30.3%; 95% CI, 26.1%-34.7%) had data source disagreement: 230 individuals (27.2%; 95% CI, 23.2%-31.5%) had a visit in PRAMS without a Medicaid claim, and 23 individuals (3.1%; 95% CI, 1.8%-5.2%) had a Medicaid claim without a visit in PRAMS. Compared with individuals continuously enrolled in Medicaid, those who transitioned to private insurance after delivery and those who were uninsured after delivery and had Emergency Medicaid at delivery had an increase in the probability of data source agreement of 15.8 percentage points (95% CI, 2.6-29.1 percentage points) and 37.2 percentage points (95% CI, 19.6-54.8 percentage points), respectively. This study's findings suggest that Medicaid claims may undercount postpartum visits among people who lose Medicaid or switch to private insurance after childbirth. Accounting for these insurance transitions may be associated with better claims-based estimates of postpartum care.

PATTERNS OF MARIJUANA USE IN THE US OLDER ADULT POPULATION

Abstract Marijuana use is increasing in the U.S. population, including among adults 50 and older, but research on population characteristics of marijuana use in middle aged and older adults is limited. We use data from the 2018-2021 Behavioral Risk Factor Surveillance System (BRFSS) surveys, which are representative of U.S. state populations, to estimate prevalence and frequency of marijuana use by age. The data are from 39 states that included the module on marijuana use in the past 30 days (n=466,469). We also examine age patterns in use by legalization status and reasons for use of marijuana. Reported marijuana use is much lower among those ages 50 and older (6.49%) than 18-49 (16.3%). Multivariable regression analyses show that marijuana use declines with age (p&amp;lt;.001) and is lower among women (p&amp;lt;.001). Marijuana use among older adults is slightly lower in states where it is illegal compared to states that have legalized (4.6% vs. 7.1%, p&amp;lt;.001). Use of marijuana for medical reasons increases with age; nearly two-thirds of adults 80+ who use marijuana report doing so for medical reasons. Adults 80+ also report the highest frequency of use, likely reflecting greater medical need. The most common conditions reported among adults 50+ who use marijuana for medical reasons are arthritis (61%), depression (36%), asthma (20%), and COPD (18%). Our prevalence estimates of use at older ages match those from other national surveys, but our findings suggest older adults are using marijuana largely for medical reasons.

Incorporating clinical trial outcomes in health economic evaluations of disease‐modifying therapies for AD

AbstractBackgroundEvidence on cost‐effectiveness of novel therapies for Alzheimer’s disease (AD) is of increasing importance for patient access. Challenges involved in implementing treatment effects in health economic (HE) models is an important source of uncertainty around the value of new AD therapies. HE models are developed from observational studies that often include different patient populations and different endpoints and scales from clinical trials.MethodsThe International Pharmacoeconomic Collaboration on Alzheimer’s Disease (IPECAD) conducted a series of workshops with research groups developing HE models in AD, to systematically characterize methodological approaches to modelling and benchmark results from these models in standardized scenarios. We reviewed published models and contrasted the characteristics of these models with study design specifications of recent late‐stage clinical trials of AD DMT to highlight potential issues with incorporating efficacy data from clinical trials in existing HE models. Identified issues included: need for mapping between different scales used to assess cognition, function and other efficacy domains, incorporating effects on multiple domains, differences in trial populations and expected patient populations treated in routine care, and statistical methods for generating efficacy estimates for use in HE models.ResultsThree options were identified for translating trial efficacy estimates into HE models: 1) use actual trial data and model disease course post trial, 2) assume treatment reduces transition probabilities to more severe states, and 3) implement treatment effect as a proportional reduction of progression on the time scale.ConclusionsThe design of future clinical trials of DMT need to take into account the need to generate data to populate HE models. Pre‐specifying analyses of trial data for HE modeling purposes can reduce uncertainty and improve confidence in results.

Generalized linear models with structured sparsity estimators

In this paper, we introduce structured sparsity estimators for use in Generalized Linear Models. Structured sparsity estimators in the least squares loss are introduced by Stucky and van de Geer (2018). Their proofs exclusively depend on their use of fixed design and normal errors. We extend their results to debiased structured sparsity estimators with Generalized Linear Model based loss through incorporating random design and non-sub Gaussian data. Structured sparsity estimation means that penalized loss functions with a possible sparsity structure in a norm. These norms include norms generated from convex cones.Our contributions are threefold: (1) We generalize the existing oracle inequality results in penalized Generalized Linear Models; (2) We provide a feasible weighted nodewise regression proof which generalizes the results in the literature; (3) We realize that norms used in feasible nodewise regression proofs should be weaker or equal to the norms in penalized Generalized Linear Model loss.

Tusi: a new ketamine concoction complicating the drug landscape

ABSTRACT A drug concoction called tusi has emerged in Latin America and in Europe and is now beginning to acquire popularity in the United States. “Tusi” is a phonetic translation of “2C,” a series of psychedelic phenethylamines. The concoction is also sometimes referred to as “pink cocaine” as it typically comes in the form of pink powder. However, despite its name, the concoction rarely contains 2C series drugs. Multiple drug checking studies have found that the majority of tusi samples contain ketamine, often combined with 3,4-methylenedioxymethamphetamine (MDMA), methamphetamine, cocaine, opioids, and/or new psychoactive substances. The tusi phenomenon complicates the drug landscape because it has the potential to confuse both people who use it and researchers alike. People using may think the drug is 2C/2C-B, and they may also be unaware that the concoction tends to consist of ketamine and a wide variety of other drugs. Unintentional exposure to its contents can lead to increased risk of adverse effects. The tusi phenomenon also has the potential to complicate drug research as unknown exposure to drugs like ketamine and MDMA will lead to underreporting of use. A combination of self-report and toxicological testing may be needed to inform the most accurate estimates of use. Both researchers and people at risk for use need to be informed about this new concoction. Drug researchers need to be cognizant about the way they query use, and people at risk for using need to be educated about the possible contents of tusi and associated dangers.

Perspectives on valuing water quality improvements using stated preference methods

Improvements to the quality of freshwater rivers and lakes can generate a wide array of benefits, from “use values” such as recreational boating, fishing, and swimming to “nonuse values” such as improved outcomes for aquatic biodiversity. Bringing these nonmarket values into decision-making is crucial to determining appropriate levels of investment in water quality improvements. However, progress in the economic valuation of water quality benefits has lagged similar efforts to value air quality benefits, with implications for water policy. New data sources, modeling techniques, and innovation in stated preference survey methods offer notable improvements to estimates of use and nonuse benefits of improved water quality. Here, we provide a perspective on how recent applications of stated preference techniques to the valuation of the nonmarket benefits of water quality improvements have advanced the field of environmental valuation. This overview is structured around four key questions: i) What is it about water quality that we seek to value? ii) How should we design and implement the surveys which elicit individuals’ stated preferences? iii) How do we assess the validity of the findings provided by such studies? and iv) What are the contributions of these valuation exercises to public policy? In answering these questions, we make reference to the contributions provided by the papers in this Symposium.

Ambient air pollution and clinical dementia: systematic review and meta-analysis

ObjectiveTo investigate the role of air pollutants in risk of dementia, considering differences by study factors that could influence findings.DesignSystematic review and meta-analysis.Data sourcesEMBASE, PubMed, Web of Science, Psycinfo, and...

On Fitting the Lomax Distribution: A Comparison between Minimum Distance Estimators and Other Estimation Techniques

In this paper, we investigate the performance of a variety of frequentist estimation techniques for the scale and shape parameters of the Lomax distribution. These methods include traditional methods such as the maximum likelihood estimator and the method of moments estimator. A version of the maximum likelihood estimator adjusted for bias is included as well. Furthermore, an alternative moment-based estimation technique, the L-moment estimator, is included, along with three different minimum distance estimators. The finite sample performances of each of these estimators are compared in an extensive Monte Carlo study. We find that no single estimator outperforms its competitors uniformly. We recommend one of the minimum distance estimators for use with smaller samples, while a bias-reduced version of maximum likelihood estimation is recommended for use with larger samples. In addition, the desirable asymptotic properties of traditional maximum likelihood estimators make them appealing for larger samples. We include a practical application demonstrating the use of the described techniques on observed data.

Composite Likelihoods with Bounded Weights in Extrapolation of Data

ABSTRACT Among many efforts to facilitate timely access to safe and effective medicines to children, increased attention has been given to extrapolation. Loosely, it is the leveraging of conclusions or available data from adults or older age groups to draw conclusions for the target pediatric population when it can be assumed that the course of the disease and the expected response to a medicinal product would be sufficiently similar in the pediatric and the reference population. Extrapolation then can be characterized as a statistical mapping of information from the reference (adults or older age groups) to the target pediatric population. The translation, or loosely mapping of information, can be through a composite likelihood approach where the likelihood of the reference population is weighted by exponentiation and that this exponent is related to the value of the mapped information in the target population. The weight is bounded above and below recognizing the fact that similarity (of the disease and the expected response) is still valid despite variability of response between the cohorts. Maximum likelihood approaches are then used for estimation of parameters, and asymptotic theory is used to derive distributions of estimates for use in inference. Hence, the estimation of effects in the target population borrows information from the reference population. In addition, this manuscript also talks about how this method is related to the Bayesian statistical paradigm.

Crtrest: A command for ratio estimators of intervention effects on event rates in cluster randomized trials

We describe five asymptotically unbiased estimators of intervention effects on event rates in nonmatched and matched-pair cluster randomized trials, and we present a bias-corrected version of the estimators for use when the number of clusters is small. The estimators are the ratio of mean counts ( r 1), ratio of mean cluster-level event rates ( r 2), ratio of event rates ( r 3), double ratio of counts ( r 4), and double ratio of event rates ( r 5). r 1, r 2, and r 3 estimate the total effect, which comprises the direct and indirect effects; r 4 and r 5 estimate the direct effect. We describe a new command, crtrest, that provides these ratio estimators and their standard errors in nonmatched and matched-pair cluster randomized trials.

Application of Defined Approaches to Assess Skin Sensitization Potency of Isothiazolinone Compounds.

Isothiazolinones (ITs) are widely used as antimicrobial preservatives in cosmetics and as additives for preservation of consumer and industrial products to control bacteria, fungi, and algae. Although they are effective biocides, they have the potential to produce skin irritation and sensitization, which poses a human health hazard. In this project, we evaluated nonanimal defined approaches (DAs) for skin sensitization that can provide point-of-departure estimates for use in quantitative risk assessment for ITs. The skin sensitization potential of six ITs was evaluated using three internationally harmonized nonanimal test methods: the direct peptide reactivity assay, KeratinoSens™, and the human cell line activation test. Results from these test methods were then applied to two versions of the Shiseido Artificial Neural Network DA. Sensitization hazard or potency predictions were compared with those of the in vivo murine local lymph node assay (LLNA). The nonanimal methods produced skin sensitization hazard and potency classifications concordant with those of the LLNA. EC3 values (the estimated concentration needed to produce a stimulation index of three, the threshold positive response) generated by the DAs had less variability than LLNA EC3 values, and confidence limits from the DAs overlapped those of the LLNA EC3 for most substances. The application of in silico models to in chemico and in vitro skin sensitization data is a promising data integration procedure for DAs to support hazard and potency classification and quantitative risk assessment.

Value transfer in ecosystem accounting applications

Ecosystem accounting is a statistical framework that aims to track the state of ecosystems and ecosystem services, with periodic updates. This framework follows the statistical standard of the System of Environmental Economic Accounting Ecosystem Accounting (SEEA EA). SEEA EA is composed of physical ecosystem extent, condition and ecosystem service supply-use accounts and monetary ecosystem service and asset accounts. This paper focuses on the potential use of the “Value Transfer” (VT) valuation method to produce the monetary ecosystem service accounts, taking advantage of experience with rigorous benefit transfer methods that have been developed and tested over many years in environmental economics. Although benefit transfer methods have been developed primarily for welfare analysis, the underlying techniques and advantages are directly applicable to monetary exchange values required for ecosystem accounting. The compilation of regular accounts is about to become a key area of work for the National Statistical Offices worldwide as well as for the EU Member States in particular, due to the anticipated amendment to regulation on European environmental economic accounts introducing ecosystem accounts. On this basis, accounting practitioners have voiced their concerns in a global consultation during SEEA EA revision, about three issues in particular: the lack of resources, the need for guidelines and the challenge of periodically updating the accounts. We argue that VT can facilitate empirical applications that assess ecosystem services in monetary terms, especially at national scales and in situations with limited expertise and resources available. VT is a low-cost valuation approach in line with SEEA EA requirements able to provide periodic, rigorous and consistent estimates for use in accounts. While some methodological challenges remain, it is likely that VT can help to implement SEEA EA at scale and in time to respond to the pressing need to incorporate nature into mainstream decision-making processes.

A Bayesian two-stage approach to integrate simulator data and expert judgment in human error probability estimation

With the ongoing efforts to collect new data for Human Reliability Analysis (HRA) (in particular, from nuclear power plant control room simulators), it becomes important that the coming data will be processed traceably, addressing its underlying variability, eventually in combination with expert judgment. In this direction, this work presents a two-stage Bayesian model to integrate expert-elicited probability estimates and empirical evidence from simulator data in the quantification of HEP values and of the associated variability distributions. The general aim is to provide a data aggregation framework able to mathematically combine diverse information sources throughout the HEP estimation process, in a systematic and reproducible way, contributing to strengthening the empirical basis of future HRA methods. The Bayesian model can be used to produce reference values and bounds for HRA methods as well as to improve the quality of plant-specific HEP estimates for use in Probabilistic Safety Assessment applications. The model is first verified with artificial data and then applied to quantify the HEP of human failure events from literature. Model sensitivity to biases in expert estimates is also investigated.

Abstract 11412: Use of Guideline-Directed Medical Therapies After Hospitalization for Heart Failure: Real-World Insights From EVOLUTION HF

Introduction: In the period after hospitalization for heart failure (hHF), patients are at high risk of cardiovascular death and rehospitalization. Guidelines for heart failure with reduced ejection fraction (HFrEF) emphasize early initiation of four guideline-directed medical therapies (GDMTs: beta-blocker, renin-angiotensin system inhibitors [RASis] or angiotensin receptor neprilysin inhibitor, mineralocorticoid receptor antagonists, and sodium-glucose co-transporter-2 inhibitors [SGLT2is]). However, real-world data on the concurrent use of these four GDMTs are scarce, particularly after hHF. Methods: EVOLUTION HF is an observational, longitudinal cohort study using claims and electronic health record databases to assess the use of GDMTs in patients with a recorded hHF. We report KM estimates of initiation of GDMTs in the 3 months following an incident hHF (index date) with no prior HF diagnosis. Use of GDMTs in the month before the index hHF (e.g. RASis for hypertension) was counted as use at index. The study period started after approval of the first SGLT2i (dapagliflozin) for HFrEF in each country. Results: Overall, 49 807 patients were included. Ejection fraction was not recorded for most patients. Mean KM estimates for use of four GDMTs at 1 and 3 months post hHF discharge were 6.5% and 7.8% (Japan), 8.0% and 11.7% (Sweden), and 0.9% and 1.7% (USA), increasing with each consecutive calendar quarter (Figure). This trend was largely explained by an increase in SGLT2i use. In the first and last quarter of each study period, KM estimates of SGLT2i use at 3 months were 15.3% and 21.2% (Japan), 7.2% and 32.6% (Sweden), and 2.3% and 4.8% (USA). Conclusions: Uptake of four GDMTs remains low, highlighting an urgent need for earlier and increased GDMT initiation to manage severe risks, especially in the vulnerable post-hHF period. Data on SGLT2i benefits have led to a slow yet steady increase in SGLT2i early use post hHF as part of a four-GDMT approach to treating HF.

Using Filters to Estimate Regional Lung Deposition with Pressurized Metered Dose Inhalers.

To evaluate the suitability of a recently proposed apparatus that uses filters to directly fractionate the in vitro lung dose into regional deposition estimates for use with pressurized metered dose inhaler (pMDI) devices as a less resource intensive alternative to cascade impaction. Using three commercially available pMDI devices (Asmanex HFA, Ventolin HFA, QVAR), regional deposition estimates were measured directly using the filter-based apparatus (FBA). Regional deposition estimates were also generated for the same inhalers by performing cascade impaction measurements and inputting the results to an in silico regional deposition model. Regional deposition for each inhaler was evaluated at an inhalation flow rate of 30 and 60 L/min. Total recovery of active pharmaceutical ingredient and extrathoracic deposition was independent of method used. The regional deposition estimates provided by each method were similar and captured the same trends. The direct measurement of estimated regional deposition is possible when using the FBA. This method is far less resource intensive than existing methods and so may be useful both for comparison of generic alternatives and the development of innovative products.

The FAO-UN Women partnership to support countries in producing sex-disaggregated data on agricultural landownership: SDG indicator 5.a.1

At the beginning of 2021, the global SDG database contained values of the indicator 5.a.11 for only 10 countries. To address this gap, UN Women and FAO launched a concerted effort to accelerate national reporting on the indicators through a multi-pronged strategy. Starting in mid-2021, the initiative supported 10 pilot countries in Asia, the Pacific and Latin America to assess their data systems and determine the suitability of existing data collection vehicles for gathering the required data. The ultimate objective was to integrate an appropriately designed short module in an ongoing national survey in a large number of countries. In addition, the FAO-UN Women initiative developed methods for generating proxy measures from internationally-supported national surveys that gather information on agricultural land ownership but not sufficient enough to estimate 5.a.1 according to the internationally agreed methodology endorsed by the UN Statistical Commission. This exercise provided new initial estimates for use in global reporting for 23 countries. This paper describes the FAO-UN Women efforts, demonstrating how they have increased the capacity of national statistical systems to collect data for estimating 5.a.1. The paper offers recommendations on how existing surveys and agricultural censuses can integrate the necessary data to accurately produce indicator 5.a.1, drawing from the technical results of the work with pilot countries.