ImportanceMillions of economically disadvantaged children depend on Medicaid for dental care, with states differing in whether they deliver these benefits using fee-for-service or capitated managed care payment models. However, there is limited research examining the association between managed care and the accessibility of dental services.ObjectiveTo estimate the association between the adoption of managed care for dental services in Florida’s Medicaid program and nontraumatic dental emergency department visits and associated charges.Design, Setting, and ParticipantsThis cohort study used an event-study difference-in-differences design, leveraging Florida Medicaid’s staggered adoption of managed care to examine its association with pediatric nontraumatic dental emergency department visits and associated charges. This study included all Florida emergency department visits from 2010 to 2014 in which the patient was 17 years or younger, the patient was a Florida resident, Medicaid paid for the visit, and a primary or secondary International Classification of Diseases, Ninth Revision, code was used to classify a nontraumatic dental condition. Analyses were conducted between May 2023 and April 2024.ExposureThe county of residence transitioning Medicaid dental services from fee-for-service to a fully capitated managed care program managed by a dental plan.Main Outcomes and MeasuresThe rate of nontraumatic dental emergency department visits per 100 000 pediatric Medicaid enrollees and the associated mean charges per visit. Nontraumatic dental emergency department visits are a well-documented proxy for access to dental care. Data on emergency department visit counts came from the Florida Agency for Health Care Administration. Medicaid population denominators were derived from the American Community Survey’s 5-year estimates.ResultsAmong the 34 414 pediatric nontraumatic dental emergency department visits that met inclusion criteria across Florida’s 67 counties, the mean (SD) age of patients was 8.11 (5.28) years, and 50.8% of patients were male. Of these, 10 087 visits occurred in control counties and 24 327 in treatment counties. Control counties generally had lower rates of NTDC ED visits per 100 000 enrollees compared with treatment counties (123.5 vs 132.7). Over the first 2.5 years of implementation, the adoption of managed care was associated with an 11.3% (95% CI, 4.0%-18.4%; P = .002) increase in nontraumatic dental emergency department visits compared with pre-implementation levels. There was no evidence that the average charge per visit changed.Conclusions and RelevanceIn this cohort study, Florida Medicaid’s adoption of managed care for pediatric dental services was associated with increased emergency department visits for children, which could be associated with decreased access to dental care.

Aim: Although the US FDA encourages manufacturers of medical devices to submit real-world evidence (RWE) to support regulatory decisions, the ability of real-world data (RWD) to generate evidence suitable for decision making remains unclear. The 2017 Medical Device User Fee Amendments (MDUFA IV), authorized the National Evaluation System for health Technology Coordinating Center (NESTcc) to conduct pilot projects, or ‘Test-Cases’, to assess whether current RWD captures the information needed to answer research questions proposed by industry stakeholders. We synthesized key lessons about the challenges conducting research with RWD and the strategies used by research teams to enhance their ability to generate evidence from RWD based on 18 Test-Cases conducted between 2020 and 2022. Materials & methods: We reviewed study protocols and reports from each Test-Case team and conducted 49 semi-structured interviews with representatives of participating organizations. Interview transcripts were coded and thematically analyzed. Results: Challenges that stakeholders encountered in working with RWD included the lack of unique device identifiers, capturing key data elements and their appropriate meaning in structured data, limited reliability of diagnosis and procedure codes in structured data, extracting information from unstructured electronic health record (EHR) data, limited capture of long-term study end points, missing data and data sharing. Successful strategies included using manufacturer and supply chain data, leveraging clinical registries and registry reporting processes to collect and aggregate data, querying standardized EHR data, implementing natural language processing algorithms and using multidisciplinary research teams. Conclusion: The Test-Cases identified numerous challenges working with RWD but also opportunities to address these challenges and improve researchers' ability to use RWD to generate evidence on medical devices.

Crime -free housing policies attempt to prevent crime within rental properties by enrolling property owners in a local crime -free housing program, which subsequently permits landlords to use a supplemental lease agreement stating certain activities that could lead to a tenant being evicted. Building on third -party policing strategies, crime -free housing policies are widely prevalent across the United States, with an estimated 2,000 jurisdictions adopting them since 1992. Despite the widespread adoption of such policies, no previous research has identified their effect on evictions. This article analyzes the effect of crime -free housing policies on evictions in four locations (Fremont, Hayward, Riverside, and San Diego County) in California. The authors obtained geocoded data on evictions through Public Records Act requests submitted to sheriff's departments in California seeking writs of execution, with additional Public Records Act requests submitted to municipalities to obtain policy implementation information, including the location of certified multifamily property units. To identify a causal effect, a spatial first differences design was used to exploit variation between U.S. Census Bureau block groups with and without certified properties. The results show that block groups with crime -free housing certified rental units have lower per capita income and larger proportions of Black and Latin/Hispanic populations. In each location, model results indicate that crime -free housing policies significantly increase evictions. Considered jointly, the findings suggest that crime -free housing policies increase evictions by 24.9 percent (95 -percent confidence interval: 15.1-34.6 percent) within treated block groups. Given the harm that evictions cause and the governmental costs of eviction proceedings, municipalities across the United States should weigh the benefits of crime -free housing policies against increases in evictions. In addition, given the close policy similarities between crime -free housing policies, criminal activity nuisance ordinances, chronic nuisance ordinances, and the one -strike policy in public housing, these results indicate that policymakers should consider revising the existing policies as a potential means to reduce evictions nationally.

The one-year U.S. Equity-First Vaccination Initiative (EVI), launched in April 2021, aimed to reduce racial inequities in coronavirus disease 2019 (COVID-19) vaccination across five demonstration cities (Baltimore, Chicago, Houston, Newark, and Oakland) and over the longer term strengthen the United States' public health system to achieve more-equitable outcomes. This initiative comprised nearly 100 community-based organizations (CBOs), who led hyper-local work to increase vaccination access and confidence in communities of individuals who identify as Black, Indigenous, and People of Color. In this study, the second of two on the initiative, the authors examine the results of the EVI. They look at the initiative's activities, effects, and challenges, and provide recommendations for how to support and sustain this hyper-local community-led approach and strengthen the public health system in the United States.

The COVID-19 pandemic highlighted the critical role of human behavior in influencing infectious disease transmission and the need for models capturing this complex dynamic. We present an agent-based model integrating an epidemiological simulation of disease spread with a cognitive architecture driving individual mask-wearing decisions. Agents decide whether to mask based on a utility function weighting factors like peer conformity, personal risk tolerance, and mask-wearing discomfort. By conducting experiments systematically varying behavioral model parameters and social network structures, we demonstrate how adaptive decision-making interacts with network connectivity patterns to impact population-level infection outcomes. The model provides a flexible computational framework for gaining insights into how behavioral interventions like mask mandates may differentially influence disease spread across communities with diverse social structures. Findings highlight the importance of integrating realistic human decision processes in epidemiological models to inform policy decisions during public health crises.

Background Emerging artificial intelligence (AI) applications have the potential to improve health, but they may also perpetuate or exacerbate inequities. Objective This review aims to provide a comprehensive overview of the health equity issues related to the use of AI applications and identify strategies proposed to address them. Methods We searched PubMed, Web of Science, the IEEE (Institute of Electrical and Electronics Engineers) Xplore Digital Library, ProQuest U.S. Newsstream, Academic Search Complete, the Food and Drug Administration (FDA) website, and ClinicalTrials.gov to identify academic and gray literature related to AI and health equity that were published between 2014 and 2021 and additional literature related to AI and health equity during the COVID-19 pandemic from 2020 and 2021. Literature was eligible for inclusion in our review if it identified at least one equity issue and a corresponding strategy to address it. To organize and synthesize equity issues, we adopted a 4-step AI application framework: Background Context, Data Characteristics, Model Design, and Deployment. We then created a many-to-many mapping of the links between issues and strategies. Results In 660 documents, we identified 18 equity issues and 15 strategies to address them. Equity issues related to Data Characteristics and Model Design were the most common. The most common strategies recommended to improve equity were improving the quantity and quality of data, evaluating the disparities introduced by an application, increasing model reporting and transparency, involving the broader community in AI application development, and improving governance. Conclusions Stakeholders should review our many-to-many mapping of equity issues and strategies when planning, developing, and implementing AI applications in health care so that they can make appropriate plans to ensure equity for populations affected by their products. AI application developers should consider adopting equity-focused checklists, and regulators such as the FDA should consider requiring them. Given that our review was limited to documents published online, developers may have unpublished knowledge of additional issues and strategies that we were unable to identify.

Preventing Chronic Disease (PCD) is a peer-reviewed electronic journal established by the National Center for Chronic Disease Prevention and Health Promotion. PCD provides an open exchange of information and knowledge among researchers, practitioners, policy makers, and others who strive to improve the health of the public through chronic disease prevention.

The RAND Corporation is a research organization that develops solutions to public policy challenges to help

Research into using artificial intelligence (AI) in health care is growing and several observers predicted that AI would play a key role in the clinical response to the COVID-19. Many AI models have been proposed though previous reviews have identified only a few applications used in clinical practice. In this study, we aim to (1) identify and characterize AI applications used in the clinical response to COVID-19; (2) examine the timing, location, and extent of their use; (3) examine how they relate to pre-pandemic applications and the U.S. regulatory approval process; and (4) characterize the evidence that is available to support their use. We searched academic and grey literature sources to identify 66 AI applications that performed a wide range of diagnostic, prognostic, and triage functions in the clinical response to COVID-19. Many were deployed early in the pandemic and most were used in the U.S., other high-income countries, or China. While some applications were used to care for hundreds of thousands of patients, others were used to an unknown or limited extent. We found studies supporting the use of 39 applications, though few of these were independent evaluations and we found no clinical trials evaluating any application’s impact on patient health. Due to limited evidence, it is impossible to determine the extent to which the clinical use of AI in the pandemic response has benefited patients overall. Further research is needed, particularly independent evaluations on AI application performance and health impacts in real-world care settings.

Background There have been over 200 million cases and 4.4 million deaths from COVID-19 worldwide. Despite the lack of robust evidence one potential treatment for COVID-19 associated severe hypoxaemia is inhaled pulmonary vasodilator (IPVD) therapy, using either nitric oxide (iNO) or prostaglandins. We describe the implementation of, and outcomes from, a protocol using IPVDs in a cohort of patients with severe COVID-19 associated respiratory failure receiving maximal conventional support. Methods Prospectively collected data from adult patients with SARS-CoV-2 admitted to the intensive care unit (ICU) at a large teaching hospital were analysed for the period 14th March 2020 - 11th February 2021. An IPVD was considered if the PaO2/FiO2 (PF) ratio was less than 13.3kPa despite maximal conventional therapy. Nitric oxide was commenced at 20ppm and titrated to response. If oxygenation improved Iloprost nebulisers were commenced and iNO weaned. The primary outcome was percentage changes in PF ratio and Alveolar-arterial (A-a) gradient. Results Fifty-nine patients received IPVD therapy during the study period. The median PF ratio before IPVD therapy was commenced was 11.33kPa (9.93-12.91). Patients receiving an IPVD had a lower PF ratio (14.37 vs. 16.37kPa, p = 0.002) and higher APACHE-II score (17 vs. 13, p = 0.028) at ICU admission. At 72 hours after initiating an IPVD the median improvement in PF ratio was 33.9% (-4.3-84.1). At 72 hours changes in PF ratio (70.8 vs. −4.1%, p < 0.001) and reduction in A-a gradient (44.7 vs. 14.8%, p < 0.001) differed significantly between survivors (n = 33) and non-survivors (n = 26). Conclusions The response to IPVDs in patients with COVID-19 associated acute hypoxic respiratory failure differed significantly between survivors and non-survivors. Both iNO and prostaglandins may offer therapeutic options for patients with severe refractory hypoxaemia due to COVID-19. The use of inhaled prostaglandins, and iNO where feasible, should be studied in adequately powered prospective randomised trials.

The coronavirus disease 2019 pandemic required significant public health interventions from local governments. Early in the pandemic, RAND researchers developed a decision support tool to provide policymakers with insight into the trade-offs they might face when choosing among nonpharmaceutical intervention levels. Using an updated version of the model, the researchers performed a stress-test of a variety of alternative reopening plans, using California as an example. This article presents the general lessons learned from these experiments and discusses four characteristics of the best reopening strategies.

Policymakers in Connecticut are considering various options to increase the affordability of insurance in the state, such as expansions to premium and cost-sharing reduction subsidies on the state's health insurance marketplace, as well as expanded plan offerings, including extending eligibility for the state employee health plan (SEHP) to other groups and a publicly contracted, privately operated plan (the public option plan) offered to individuals on the marketplace. The authors used the RAND Corporation's COMPARE microsimulation model to estimate the impacts of such policy options. For each policy scenario, they calculated enrollment, premiums, consumer spending, and state spending and considered whether the results differed by race, ethnicity, or income group. The individual market reforms substantially increased affordability for people with incomes between 175 and 200 percent of the federal poverty level (FPL), reducing out-of-pocket spending as a share of income by 50 percent in some scenarios. Changes to affordability for higher-income groups were smaller, in part because the proposed policy changes for people with incomes between 200 and 400 percent of FPL were relatively modest and focused only on reducing cost-sharing (not premiums). New costs to the state for 2023 ranged from $19 million to $94 million, depending on the scenario. All four SEHP specifications led to the same bottom-line conclusion that offering a SEHP plan would improve insurance coverage and affordability for those eligible for the plan. Expanding eligibility for the SEHP holds promise for stabilizing or reducing consumer costs, improving plan generosity, and bringing more people into the market.

We investigated racial and ethnic disparities in COVID-19 vaccine uptake, using data from the Centers for Disease Control and Prevention. As of March 29, 2022, uptake of the first dose was higher among Hispanic and Asian people than among White and Black people. In contrast, uptake rates of the booster were higher among Asian and White people than among Black and Hispanic people.

Amid global scarcity of COVID-19 vaccines and the threat of new variant strains, California and other jurisdictions face the question of when and how to implement and relax COVID-19 Nonpharmaceutical Interventions (NPIs). While policymakers have attempted to balance the health and economic impacts of the pandemic, decentralized decision-making, deep uncertainty, and the lack of widespread use of comprehensive decision support methods can lead to the choice of fragile or inefficient strategies. This paper uses simulation models and the Robust Decision Making (RDM) approach to stress-test California's reopening strategy and other alternatives over a wide range of futures. We find that plans which respond aggressively to initial outbreaks are required to robustly control the pandemic. Further, the best plans adapt to changing circumstances, lowering their stringent requirements to reopen over time or as more constituents are vaccinated. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower.

We developed a COVID-19 transmission model used as part of RAND's web-based COVID-19 decision support tool that compares the effects of nonpharmaceutical public health interventions (NPIs) on health and economic outcomes. An interdisciplinary approach informed the selection and use of multiple NPIs, combining quantitative modeling of the health/economic impacts of interventions with qualitative assessments of other important considerations (e.g., cost, ease of implementation, equity). This paper provides further details of our model, describes extensions, presents sensitivity analyses, and analyzes strategies that periodically switch between a base NPI level and a higher NPI level. We find that a periodic strategy, if implemented with perfect compliance, could have produced similar health outcomes as static strategies but might have produced better outcomes when considering other measures of social welfare. Our findings suggest that there are opportunities to shape the tradeoffs between economic and health outcomes by carefully evaluating a more comprehensive range of reopening policies.

Real-Time polymerase chain reaction (qPCR) is the gold standard diagnostic method for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection. Cycle threshold (Ct) is defined as the number of heating and cooling cycles required during the PCR process. Ct-values are inversely proportional to the amount of target nucleic acid in a sample. Our aim, in this retrospective study, was to determine the impact of serial SARS-CoV-2 qPCR Ct-values on: mortality, need for mechanical ventilation (MV) and development of acute kidney injury (AKI) in patients admitted to the intensive care unit (ICU) with COVID-19. Ct values were evaluated during the time points from pre-ICU admission to week 1, week 2 and week 3 during ICU stay; impact on mortality, need for MV and AKI was determined. There was a continuous increment in Ct-values over the ICU stay from 1st week through to 3rd week. Although not significant, lower ICU 1st week Ct-values were associated with Black ethnicity, increased need for MV and mortality. However, patients who had developed AKI at any stage of their illness had significantly lower Ct-values compared to those with normal renal function. When ICU 1st-week Ct-values are subcategorised as <20, 20-30 and >30 the 28-day survival probability was less for patients with Ct-values of <20. This report shows that the impact of Ct-values and outcomes, especially AKI, among patients at different time points prior to and during ICU stay, larger studies are required to confirm out findings.

We developed a COVID-19 transmission model to compare the effects of nonpharmaceutical public health interventions (NPIs) on health and economic outcomes. An interdisciplinary approach informed the selection and use of multiple NPIs, combining quantitative modeling of the health and economic impacts of interventions with qualitative assessments of other important considerations (e.g., cost, ease of implementation, equity). We used our model to analyzed strategies that periodically switch between a base NPI and a high NPI level. We find that this systematic strategy could have produced similar health outcomes as static strategies but better social welfare and economic outcomes. Our findings suggest that there are opportunities to shape the tradeoffs between economic and health outcomes by carefully evaluating a more comprehensive range of reopening policies.

The COVID-19 pandemic required significant public health interventions from local governments. Although nonpharmaceutical interventions often were implemented as decision rules, few studies evaluated the robustness of those reopening plans under a wide range of uncertainties. This paper uses the Robust Decision Making approach to stress-test 78 alternative reopening strategies, using California as an example. This study uniquely considers a wide range of uncertainties and demonstrates that seemingly sensible reopening plans can lead to both unnecessary COVID-19 deaths and days of interventions. We find that plans using fixed COVID-19 case thresholds might be less effective than strategies with time-varying reopening thresholds. While we use California as an example, our results are particularly relevant for jurisdictions where vaccination roll-out has been slower. The approach used in this paper could also prove useful for other public health policy problems in which policymakers need to make robust decisions in the face of deep uncertainty.