Comparing frequency of booster vaccination to prevent severe COVID-19 by risk group in the United States.

There is a public health need to understand how different frequencies of COVID-19 booster vaccines may mitigate the risk of severe COVID-19, while accounting for waning of protection and differential risk by age and immune status. By analyzing United States COVID-19 surveillance and seroprevalence data in a microsimulation model, here we show that more frequent COVID-19 booster vaccination (every 6-12 months) in older age groups and the immunocompromised population would effectively reduce the burden of severe COVID-19, while frequent boosters in the younger population may only provide modest benefit against severe disease. In persons 75+ years, the model estimated that annual boosters would reduce absolute annual risk of severe COVID-19 by 199 (uncertainty interval: 183-232) cases per 100,000 persons, compared to a one-time booster vaccination. In contrast, for persons 18-49 years, the model estimated that annual boosters would reduce this risk by 14 (10-19) cases per 100,000 persons. Those with prior infection had lower benefit of more frequent boosting, and immunocompromised persons had larger benefit. Scenarios with emerging variants with immune evasion increased the benefit of more frequent variant-targeted boosters. This study underscores the benefit of considering key risk factors to inform frequency of COVID-19 booster vaccines in public health guidance and ensuring at least annual boosters in high-risk populations.

Accessibility of Opioid Treatment Programs Based on Conventional vs Perceived Travel Time Measures.

Importance: Transportation barriers have long been associated with poorer health outcomes; this burden is especially acute for individuals with opioid use disorder (OUD), a chronic disease often associated with low socioeconomic status. Conventional travel time analyses may not fully account for experiential components of travel, thereby understating the true travel burden and overstating treatment accessibility to opioid treatment programs (OTPs). Objective: To develop a metric of feels-like accessibility for those using public transit to access OTPs that accounts for the realistic travel burden on individuals with OUD. Design, Setting, and Participants: This cross-sectional study integrated high-resolution transit schedules and operating hours of OTPs to measure feels-like accessibility. Feels-like accessibility considers the differential outcomes of out-of-vehicle travel components and more realistically reflects individuals' transportation burden than conventional accessibility measures. Gini indices and spatial regression models were used to investigate inequities in accessibility. Geocoded data for residential addresses of 1018 overdose fatalities in Connecticut in 2019 were used as a proxy for the treatment needs of individuals with OUD. Data were analyzed between May and August 2023. Main Outcomes and Measures: Conventional and feels-like accessibility scores. Exposures: Fluctuations in public transit frequencies over the course of the day and the limited operating hours of the OTPs. Results: Of the 1018 individuals in the study, the mean (SD) age at death was 43.7 (12.6) years, 784 individuals (77%) were men, 111 (11%) were African American, and 889 (87%) were White, with other racial and ethnic categories including 18 individuals (2%). A total of 264 individuals in the sample (26%) could not access an OTP within 180 minutes. For those who could access these facilities, the average 1-way travel time was 45.6 minutes, with individuals spending approximately 70% of their trip duration on out-of-vehicle travel components. The conventional accessibility metric underestimates individuals' travel burden to OTPs as well as the inequity in accessibility compared with the feels-like accessibility metric. For example, the median (range) conventional accessibility score, defined as the number of OTPs within 120 minutes of transit travel time, was 5.0 (0.0-17.0); the median (range) feels-like accessibility score, defined as the number of OTPs within 120 minutes of transit travel time weighted to account for in- and out-of-vehicle segments, was 1.0 (0.0-10.0). There is a considerable temporal variation in travel time and accessibility depending on the departure times. Conclusions and Relevance: In this cross-sectional study of travel burdens, the calculated feels-like accessibility scores, which consider the differential outcomes of out-of-vehicle travel components (eg, walking and waiting), could better and more realistically reflect passengers' transportation burden. Policy recommendations derived from the conventional accessibility metric could be misleading, and decision-makers should use feels-like accessibility metrics that adequately capture individuals' travel burdens. In the context of access to OTPs, the findings from this study suggest that opening new OTP sites to address gaps in access due to distance to services or extending hours of operation at existing sites may ameliorate the travel burden for individuals.

Clade I-Associated Mpox Cases Associated with Sexual Contact, the Democratic Republic of the Congo.

We report a cluster of clade I monkeypox virus infections linked to sexual contact in the Democratic Republic of the Congo. Case investigations resulted in 5 reverse transcription PCR-confirmed infections; genome sequencing suggest they belonged to the same transmission chain. This finding demonstrates that mpox transmission through sexual contact extends beyond clade IIb.

Selection Bias Requires Selection: The Case of Collider Stratification Bias.

In epidemiology, collider stratification bias, the bias resulting from conditioning on a common effect of two causes, is oftentimes considered a type of selection bias, regardless of the conditioning methods employed. In this commentary, we distinguish between two types of collider stratification bias: collider restriction bias due to restricting to one level of a collider (or a descendant of a collider) and collider adjustment bias through inclusion of a collider (or a descendant of a collider) in a regression model. We argue that categorizing collider adjustment bias as a form of selection bias may lead to semantic confusion, as adjustment for a collider in a regression model does not involve selecting a sample for analysis. Instead, we propose that collider adjustment bias can be better viewed as a type of overadjustment bias. We further provide two distinct causal diagram structures to distinguish collider restriction bias and collider adjustment bias. We hope that such a terminological distinction can facilitate easier and clearer communication.

Mortality, incarceration and cost implications of fentanyl felonization laws: A modeling study.

BACKGROUND: Opioid overdose continues to be a major cause of death in the United States. One effort to control opioid use has been to implement policies that enhance criminalization of opioid possession. Laws to further criminalize possession of fentanyl have been enacted or are under consideration across the country, including at the national level. OBJECTIVE: Estimate the long-term effects on opioid death and incarceration resulting from increasingly strict fentanyl possession laws . DESIGN: We built a Markov simulation model to explore the potential outcomes of a 2022 Colorado law which made possession of >1 g of drug with any amount of fentanyl a Level 4 drug felony (and escalation of the previous law, where >4 g of any drug with any amount of fentanyl in possession was considered a felony). The model simulates a cohort of people with fentanyl possession moving through the criminal justice system, exploring the probability of overdose and incarceration under different scenarios, including various fentanyl possession policies and potential interventions. SETTING: Colorado PARTICIPANTS: A simulated cohort of people in possession of fentanyl. MEASUREMENTS: Number of opioid overdose deaths, people incarcerated, and associated costs over 5 years. RESULTS: When >4 g of a drug containing any amount of fentanyl is considered a felony in Colorado, the model predicts 5460 overdose deaths (95% CrI 410-9260) and 2,740 incarcerations for fentanyl possession (95% CrI: 230-10,500) over 5 years. When the policy changes so that >1 g possession of drug with fentanyl is considered a felony, opioid overdose deaths increase by 19% (95% CRI: 16-38%) and incarcerations for possession increase by 98% (CrI: 85-98%). Diversion programs and MOUD in prison help alleviate some of the increases in death and incarceration, but do not completely offset them. LIMITATIONS: The mathematical model is meant to offer broad assessment of the impact of these policies, not forecast specific and exact numerical outcomes. CONCLUSIONS: Our model shows that lowering thresholds for felony possession of fentanyl containing drugs can lead to more opioid overdose deaths and incarceration.

Using Bandit Algorithms to Maximize SARS-CoV-2 Case-Finding: Evaluation and Feasibility Study.

BACKGROUND: The Flexible Adaptive Algorithmic Surveillance Testing (FAAST) program represents an innovative approach for improving the detection of new cases of infectious disease; it is deployed here to screen and diagnose SARS-CoV-2. With the advent of treatment for COVID-19, finding individuals infected with SARS-CoV-2 is an urgent clinical and public health priority. While these kinds of Bayesian search algorithms are used widely in other settings (eg, to find downed aircraft, in submarine recovery, and to aid in oil exploration), this is the first time that Bayesian adaptive approaches have been used for active disease surveillance in the field. OBJECTIVE: This study's objective was to evaluate a Bayesian search algorithm to target hotspots of SARS-CoV-2 transmission in the community with the goal of detecting the most cases over time across multiple locations in Columbus, Ohio, from August to October 2021. METHODS: The algorithm used to direct pop-up SARS-CoV-2 testing for this project is based on Thompson sampling, in which the aim is to maximize the average number of new cases of SARS-CoV-2 diagnosed among a set of testing locations based on sampling from prior probability distributions for each testing site. An academic-governmental partnership between Yale University, The Ohio State University, Wake Forest University, the Ohio Department of Health, the Ohio National Guard, and the Columbus Metropolitan Libraries conducted a study of bandit algorithms to maximize the detection of new cases of SARS-CoV-2 in this Ohio city in 2021. The initiative established pop-up COVID-19 testing sites at 13 Columbus locations, including library branches, recreational and community centers, movie theaters, homeless shelters, family services centers, and community event sites. Our team conducted between 0 and 56 tests at the 16 testing events, with an overall average of 25.3 tests conducted per event and a moving average that increased over time. Small incentives-including gift cards and take-home rapid antigen tests-were offered to those who approached the pop-up sites to encourage their participation. RESULTS: Over time, as expected, the Bayesian search algorithm directed testing efforts to locations with higher yields of new diagnoses. Surprisingly, the use of the algorithm also maximized the identification of cases among minority residents of underserved communities, particularly African Americans, with the pool of participants overrepresenting these people relative to the demographic profile of the local zip code in which testing sites were located. CONCLUSIONS: This study demonstrated that a pop-up testing strategy using a bandit algorithm can be feasibly deployed in an urban setting during a pandemic. It is the first real-world use of these kinds of algorithms for disease surveillance and represents a key step in evaluating the effectiveness of their use in maximizing the detection of undiagnosed cases of SARS-CoV-2 and other infections, such as HIV.

COVID-19 annual update: a narrative review.

Three and a half years after the pandemic outbreak, now that WHO has formally declared that the emergency is over, COVID-19 is still a significant global issue. Here, we focus on recent developments in genetic and genomic research on COVID-19, and we give an outlook on state-of-the-art therapeutical approaches, as the pandemic is gradually transitioning to an endemic situation. The sequencing and characterization of rare alleles in different populations has made it possible to identify numerous genes that affect either susceptibility to COVID-19 or the severity of the disease. These findings provide a beginning to new avenues and pan-ethnic therapeutic approaches, as well as to potential genetic screening protocols. The causative virus, SARS-CoV-2, is still in the spotlight, but novel threatening virus could appear anywhere at any time. Therefore, continued vigilance and further research is warranted. We also note emphatically that to prevent future pandemics and other world-wide health crises, it is imperative to capitalize on what we have learnt from COVID-19: specifically, regarding its origins, the world's response, and insufficient preparedness. This requires unprecedented international collaboration and timely data sharing for the coordination of effective response and the rapid implementation of containment measures.

Early OxyContin Marketing Linked To Long-Term Spread Of Infectious Diseases Associated With Injection Drug Use.

The initial marketing of the opioid analgesic OxyContin in 1996 increased fatal drug overdoses over the course of the opioid epidemic in the US. However, the long-term impacts of this marketing on complications of injection drug use, a key feature of the ongoing crisis, are undetermined. This study evaluated the effects of exposure to initial OxyContin marketing on the long-term trajectories of injection drug use-related outcomes in the US. We used a difference-in-differences analysis to compare outcomes in states with high versus low exposure to initial marketing before and after the 2010 reformulation of OxyContin, which facilitated the use of illicit drugs and the spread of infectious disease. Exposure to initial OxyContin marketing statistically significantly increased rates of fatal synthetic opioid-related overdoses; acute hepatitis A, B, and C viral infections; and infective endocarditis-related deaths. The greatest burden of adverse long-term outcomes has been in states that experienced the highest exposure to early OxyContin marketing. Our findings indicate that OxyContin marketing decisions from the mid-1990s increased viral and bacterial complications of injection drug use and illicit opioid-related overdose deaths twenty-five years later.

Increased HIV Transmissions With Reduced Insurance Coverage for HIV Preexposure Prophylaxis: Potential Consequences of Braidwood Management v. Becerra.

A US federal court recently ruled against requiring health insurers to cover human immunodeficiency virus (HIV) preexposure prophylaxis (PrEP) under the Affordable Care Act. For every 10% decrease in PrEP coverage resulting from this ruling among US men who have sex with men, we estimate an additional 1140 HIV infections in the following year in that population.

Population-Level Health Effects of Involuntary Displacement of People Experiencing Unsheltered Homelessness Who Inject Drugs in US Cities.

Importance: At least 500 000 people in the US experience homelessness nightly. More than 30% of people experiencing homelessness also have a substance use disorder. Involuntary displacement is a common practice in responding to unsheltered people experiencing homelessness. Understanding the health implications of displacement (eg, "sweeps," "clearings," "cleanups") is important, especially as they relate to key substance use disorder outcomes. Objective: To estimate the long-term health effects of involuntary displacement of people experiencing homelessness who inject drugs in 23 US cities. Design, Setting, and Participants: A closed cohort microsimulation model that simulates the natural history of injection drug use and health outcomes among people experiencing homelessness who inject drugs in 23 US cities. The model was populated with city-level data from the Centers for Disease Control and Prevention's National HIV Behavioral Surveillance system and published data to make representative cohorts of people experiencing homelessness who inject drugs in those cities. Main Outcomes and Measures: Projected outcomes included overdose mortality, serious injection-related infections and mortality related to serious injection-related infections, hospitalizations, initiations of medications for opioid use disorder, and life-years lived over a 10-year period for 2 scenarios: "no displacement" and "continual involuntary displacement." The population-attributable fraction of continual displacement to mortality was estimated among this population. Results: Models estimated between 974 and 2175 additional overdose deaths per 10 000 people experiencing homelessness at 10 years in scenarios in which people experiencing homelessness who inject drugs were continually involuntarily displaced compared with no displacement. Between 611 and 1360 additional people experiencing homelessness who inject drugs per 10 000 people were estimated to be hospitalized with continual involuntary displacement, and there will be an estimated 3140 to 8812 fewer initiations of medications for opioid use disorder per 10 000 people. Continual involuntary displacement may contribute to between 15.6% and 24.4% of additional deaths among unsheltered people experiencing homelessness who inject drugs over a 10-year period. Conclusion and Relevance: Involuntary displacement of people experiencing homelessness may substantially increase drug-related morbidity and mortality. These findings have implications for the practice of involuntary displacement, as well as policies such as access to housing and supportive services, that could mitigate these harms.

Patterns of Infectious Disease Associated With Injection Drug Use in Massachusetts.

BACKGROUND: Since 2014, multiple outbreaks of human immunodeficiency virus (HIV) among people who inject drugs have occurred across the United States along with hepatitis C virus (HCV), skin and soft tissue infections (SSTIs), and infective endocarditis (IE), creating a converging public health crisis. METHODS: We analyzed the temporal patterns of infectious disease and overdose using a hierarchical Bayesian distributed lag logistic regression model examining the probability that a given geographic area experienced at least 1 HIV case in a given month as a function of the counts/rates of overdose, HCV, SSTI, and IE and associated medical procedures at different lagged time periods. RESULTS: Current-month HIV is associated with increasing HCV cases, abscess incision and drainage, and SSTI cases, in distinct temporal patterns. For example, 1 additional HCV case occurring 5 and 7 months previously is associated with a 4% increase in the odds of observing at least 1 current-month HIV case in a given locale (odds ratios, 1.04 [90% credible interval {CrI}: 1.01-1.10] and 1.04 [90% CrI: 1.00-1.09]). No such associations were observed for echocardiograms, IE, or overdose. CONCLUSIONS: Lagged associations in other infections preceding rises in current-month HIV counts cannot be described as predictive of HIV outbreaks but may point toward newly discovered epidemics of injection drug use and associated clinical sequalae, prompting clinicians to screen patients more carefully for substance use disorder and associated infections.

Geographic and temporal trends in fentanyl-detected deaths in Connecticut, 2009-2019.

PURPOSE: Since 2012 fentanyl-detected fatal overdoses have risen from 4% of all fatal overdoses in Connecticut to 82% in 2019. We aimed to investigate the geographic and temporal trends in fentanyl-detected deaths in Connecticut during 2009-2019. METHODS: Data on the dates and locations of accidental/undetermined opioid-detected fatalities were obtained from Connecticut Office of the Chief Medical Examiner. Using a Bayesian space-time binomial model, we estimated spatiotemporal trends in the proportion of fentanyl-detected deaths. RESULTS: During 2009-2019, a total of 6,632 opioid deaths were identified. Among these, 3234 (49%) were fentanyl-detected. The modeled spatial patterns suggested that opioid deaths in northeastern Connecticut had higher probability of being fentanyl-detected, while New Haven and its neighboring towns and the southwestern region of Connecticut, primarily Greenwich, had a lower risk. Model estimates also suggested fentanyl-detected deaths gradually overtook the preceding non-fentanyl opioid-detected deaths across Connecticut. The estimated temporal trend showed the probability of fentanyl involvement increased substantially since 2014. CONCLUSIONS: Our findings suggest that geographic variation exists in the probability of fentanyl-detected deaths, and areas at heightened risk are identified. Further studies are warranted to explore potential factors contributing to the geographic heterogeneity and continuing dispersion of fentanyl-detected deaths in Connecticut.

Estimated Testing, Tracing, and Vaccination Targets for Containment of the US Mpox Outbreak.

This decision analytic model estimates the levels of community testing, contract tracing, and vaccination required to reduce the effective reproduction number of the mpox virus among the high-risk group of men who have sex with men.

Planning for Mpox on a College Campus: A Model-Based Decision-Support Tool.

BACKGROUND: In spring and summer 2022, an outbreak of mpox occurred worldwide, largely confined to men who have sex with men (MSM). There was concern that mpox could break swiftly into congregate settings and populations with high levels of regular frequent physical contact, like university campus communities. OBJECTIVE: To estimate the likelihood of an mpox outbreak and the potential effect of mitigation measures in a residential college setting. DESIGN: A stochastic dynamic SEIR (susceptible, exposed but not infectious, infectious, or recovered) model of mpox transmission in a study population was developed, composed of: a high-risk group representative of the population of MSM with a basic reproductive number (R 0) of 2.4 and a low-risk group with an R 0 of 0.8. Base input assumptions included an incubation time of 7.6 days and time to recovery of 21 days. SETTING: U.S. residential college campus. PARTICIPANTS: Hypothetical cohort of 6500 students. INTERVENTION: Isolation, quarantine, and vaccination of close contacts. MEASUREMENTS: Proportion of 1000 simulations producing sustained transmission; mean cases given sustained transmission; maximum students isolated, quarantined, and vaccinated. All projections are estimated over a planning horizon of 100 days. RESULTS: Without mitigation measures, the model estimated an 83% likelihood of sustained transmission, leading to an average of 183 cases. With detection and isolation of 20%, 50%, and 80% of cases, the average infections would fall to 117, 37, and 8, respectively. Reactive vaccination of contacts of detected cases (assuming 50% detection and isolation) reduced mean cases from 37 to 17, assuming 20 vaccinated contacts per detected case. Preemptive vaccination of 50% of the high-risk population before outbreak reduced cases from 37 to 14, assuming 50% detection and isolation. LIMITATION: A model is a stylized portrayal of behavior and transmission on a university campus. CONCLUSION: Based on our current understanding of mpox epidemiology among MSM in the United States, this model-based analysis suggests that future outbreaks of mpox on college campuses may be controlled with timely detection and isolation of symptomatic cases. PRIMARY FUNDING SOURCE: National Institutes of Health National Institute on Drug Abuse and National Institute of Allergy and Infectious Diseases.

Prioritization of carceral spending in U.S. cities: Development of the Carceral Resource Index (CRI) and the role of race and income inequality.

BACKGROUND: Policing, corrections, and other carceral institutions are under scrutiny for driving health harms, while receiving disproportionate resources at the expense of prevention and other services. Amidst renewed interest in structural determinants of health, roles of race and class in shaping government investment priorities are poorly understood. METHODS: Based on the Social Conflict Model, we assessed relationships between city racial/ economic profiles measured by the Index of Concentration at the Extremes (ICE) and budgetary priorities measured by the novel Carceral Resource Index (CRI), contrasting investments in carceral systems with funding for health and social support across the 50 most populous cities in the United States (U.S.). Bivariate correlations, and unadjusted and adjusted polynomial regression models were used to assess the relationship between budgetary investments and population concentration at extremes in terms of income, racial/ethnic composition, and education, controlling for other demographic characteristics. RESULTS: In our sample, median CRI was -0.59 (IQR -0.64, -0.45), with only seven cities exhibiting positive CRI values. This indicates that most large U.S. cities spend more on carceral systems than on health and supportive services, combined. Adjusted polynomial models showed a convex relationship between the CRI and ICE-Education, and ICE-Race(White vs. Black)+Income, with quadratic terms that were positive and significant at p<0.05. After controlling for age, the strongest prioritization of carceral systems was observed in cities where the proportion of low-income Black residents approached or exceeded that of high-income white residents. CONCLUSIONS: Municipal prioritization of carceral investments over health and social support is pervasive in the U.S and exacerbated by racial and economic disparities. The CRI offers new opportunities to understand the role of government investments as a structural determinant of health and safety. Longitudinal research is warranted to examine the relationship between budget priorities, structural racism, and health outcomes.

Estimating deaths averted and cost per life saved by scaling up mRNA COVID-19 vaccination in low-income and lower-middle-income countries in the COVID-19 Omicron variant era: a modelling study.

OBJECTIVES: While almost 60% of the world has received at least one dose of COVID-19 vaccine, the global distribution of vaccination has not been equitable. Only 4% of the population of low-income countries (LICs) has received a full primary vaccine series, compared with over 70% of the population of high-income nations. DESIGN: We used economic and epidemiological models, parameterised with public data on global vaccination and COVID-19 deaths, to estimate the potential benefits of scaling up vaccination programmes in LICs and lower-middle-income countries (LMICs) in 2022 in the context of global spread of the Omicron variant of SARS-CoV2. SETTING: Low-income and lower-middle-income nations. MAIN OUTCOME MEASURES: Outcomes were expressed as number of avertable deaths through vaccination, costs of scale-up and cost per death averted. We conducted sensitivity analyses over a wide range of parameter estimates to account for uncertainty around key inputs. FINDINGS: Globally, universal vaccination in LIC/LMIC with three doses of an mRNA vaccine would result in an estimated 1.5 million COVID-19 deaths averted with a total estimated cost of US$61 billion and an estimated cost-per-COVID-19 death averted of US$40 800 (sensitivity analysis range: US$7400-US$81 500). Lower estimated infection fatality ratios, higher cost-per-dose and lower vaccine effectiveness or uptake lead to higher cost-per-death averted estimates in the analysis. CONCLUSIONS: Scaling up COVID-19 global vaccination would avert millions of COVID-19 deaths and represents a reasonable investment in the context of the value of a statistical life. Given the magnitude of expected mortality facing LIC/LMIC without vaccination, this effort should be an urgent priority.

Monkeypox emergency: Urgent questions and perspectives.

Amidst the COVID-19 pandemic, we now face another public health emergency in the form of monkeypox virus. As of August 1, the Centers for Disease Control and Prevention report over 23,000 cases in 80 countries. An inclusive and global collaborative effort to understand the biology, evolution, and spread of the virus as well as commitment to vaccine equity will be critical toward containing this outbreak. We share the voices of leading experts in this space on what they see as the most pressing questions and directions for the community.