Economic Research Service

Approaches to strengthen Data-to-Care (D2C) models are needed. We tested an insurance-based D2C prescription model (D2C-Rx), which used antiretroviral therapy (ART) prescription claims to identify people with HIV who fail to fill ART prescriptions in a timely manner and to provide ART adherence support. The Antiretroviral Improvement among Medicaid EnrolleeS (AIMS) study was the first trial of an insurance-based D2C-Rx program. We describe AIMS study enrollment, focusing on patient-participants in the AIMS program arm. Virginia, United States. AIMS used Virginia Medicaid claims and HIV surveillance data to identify Medicaid enrollees with HIV who were without an ART prescription and their providers; study staff then provided targeted adherence support. The target patient-participant sample size was 1000. The outcome of interest was HIV viral suppression. The study encountered difficulty recruiting patient-participants. Low enrollment prompted major changes to the protocol, including revisions to study design, eligibility criteria, and recruitment methods. These changes were unsuccessful. The trial was terminated because of low patient-participant enrollment (n = 4). Reasons for low patient-participant enrollment may include phone-based recruitment approach, lengthy identity verification and consent procedures, and lack of participant compensation. Although we aimed to identify people with HIV without a current ART prescription refill, several participants reported having their medications available; this discrepancy suggests issues with unexpected participant behavior (eg, stockpiling) or timeliness and accuracy of prescription claims data. Future insurance-based D2C-Rx interventions should account for these challenges, be tailored to context, and be nimble enough to pivot to alternative recruitment methods when needed.

Abstract Research Summary Using administrative data from the US Census Bureau, we introduce a new public‐use dataset (“Business Dynamics Statistics—High Growth” [BDS‐HG]) that captures the full distribution of firm growth in the United States between 1978 and 2021. BDS‐HG enables researchers to analyze not only high‐growth firms but also stagnant and shrinking firms, disaggregated by firm maturity, size, industry, region, and year. We begin by documenting two key trends: (1) a significant decline in the share of high‐growth firms—especially among young and small firms and (2) a marked rise in stagnant firms. To help illustrate the dataset's usefulness, we provide three empirical applications and highlight research opportunities across major areas of strategic management. In addition to supporting new analyses, BDS‐HG can be used to benchmark research samples and assess their generalizability. Managerial Summary The US economy has seen two major shifts in firm growth dynamics: a steady decline in the share of high‐growth firms—especially among startups—and a notable rise in firms that exhibit no growth. We document these patterns using BDS‐HG, a new public‐use dataset from the US Census Bureau that tracks employment growth across all US employer businesses from 1978 to 2021. The data are disaggregated by firm age, size, industry, geography, and year, enabling users to examine growth trends across sectors and regions. BDS‐HG offers a valuable resource for researchers, policymakers, and analysts seeking to understand long‐term business dynamism. It can also be used to benchmark research samples against national patterns, helping users assess how representative their data or findings are.

Affordability limits healthy diet adoption, especially in low-income settings, yet dietary shifts are key for transition to sustainable food systems. This study models how a diet in line with the 2025 EAT-Lancet Commission dietary transition might impact calorie availability, share of income spent on food, nutrition availability, and food prices. We use the International Model for Policy Analysis of Agricultural Commodities and Trade (IMPACT) to estimate food price changes under three alternative Shared Socioeconomic Pathways and global adoption of a 2025 EAT-Lancet Commission diet by 2050. We analyse price shifts for the two cheapest commodities per food group, in each region, weighted on calorie availability per dollar. Additionally, we assess gaps between nutrient availability and reference nutrient intake and changes in the share of income spent on food for the whole diet. Adoption of the 2025 EAT-Lancet Commission diet leads to heterogeneous impacts on the share of income spent on food and nutrient availability, with gains in folate, iron, and zinc, but declines in vitamin A by 2050. The combined price index for the cheapest two commodities declines by 2050 compared with 2020 in both higher-income and lower-income countries. Although dietary shifts towards the 2025 EAT-Lancet Commission diet could offset some of the increases in prices seen in a business-as-usual world, we observed unintended effects on nutrient availability ratios, especially in lower-income countries. The decreasing price index for the cheapest two commodities reflects market adjustments to changes in demand and supply under scenario assumptions aligned with 2025 EAT-Lancet Commission goals for jointly improved human and environmental health. The observed nutrient deficiencies suggest the 2025 EAT-Lancet Commission diet limits on animal-sourced foods might be too strict for lower-income countries, which could exacerbate nutrient deficiencies in contexts where access to animal sourced foods is already low (eg, vitamin A), especially if there is no access to supplementation for meeting these nutritional requirements or other sources of dietary nutrients. CGIAR Science Program on Policy Innovations (Area of Work 1-Foresight and Prioritization).

Research has documented direct negative impacts of crises, such as COVID-19, on people’s mental health. However, evidence is limited about how these events impact decision-making through direct influences on choices, or by indirectly changing decision-making through mental health effects. Research on avoidance behaviors suggests that affective states influence decisions to access healthcare and receive diagnoses. While there is significant evidence that hopelessness related to a potential health threat impacts decisions to learn about that threat, affective responses to crises may also cause spillovers to decision-making in other domains. In this study, we examine linkages between exposure to a stressor (COVID-19-related income loss), feelings of hopelessness, and foregoing or delaying healthcare across multiple cross-sections of the US Census’s Household Pulse Survey, featuring 2.76 million survey responses collected between April 23, 2020, and July 5, 2021. After removing observations with missing data for dependent variables, the final sample size is just under 2.3 million responses. We conduct ordered logistic regressions of the relationship of income loss with hopelessness levels, and logistic regression of the relationship of income loss and hopelessness levels on health care access. We additionally report versions of the regressions with demographic variables and time and state fixed effects to control for important factors related to those variables. We conduct a mediation analysis to estimate the pathway of income loss acting through hopelessness. The analyses find that experienced income loss predicts significantly higher levels of hopelessness (odds ratio (OR)=1.68 (95%CI = 1.67, 1.69)). Both hopelessness and income loss are, in turn, associated with healthcare access—an increased likelihood of foregoing and/or delaying needed medical care (e.g., hopelessness nearly every day (OR=4.18, 95%CI = 4.13, 4.23), experienced income loss OR=1.25, 95%CI = 1.24, 1.26)). A mediation analysis confirms that hopelessness significantly and consistently mediates approximately 30% of the relationship of COVID-19 income loss to foregoing/delaying healthcare.

Investment in public agricultural R&D is a key driver of agricultural productivity, which is regarded as one of the major solutions to enhance global food security, reduce the environmental impact of agricultural production and enhance crop resilience to climate change. Detailed information on public agricultural R&D investment is required to inform national science and technology policies that aim to improve the performance of the agricultural sector and monitor whether countries are on track to reach (inter)national targets. Due to the long lead-time, it requires exceptionally long time-series to assess the impact of public agricultural R&D spending on present and future agricultural productivity. To address these issues, this paper presents GRAPE - the WUR-ERS Global Research on Agriculture: Personnel & Expenditures dataset, which contains data on the number of public agricultural researchers and R&D expenditures for 190 countries, broadly covering the period 1960–2022. To construct the dataset, information from a large number of sources was combined, different R&D classifications were harmonized and missing data were imputed.