Risk factors underlying racial and ethnic disparities in tuberculosis diagnosis and treatment outcomes, 2011-19: a multiple mediation analysis of national surveillance data.

BACKGROUND: Despite an overall decline in tuberculosis incidence and mortality in the USA in the past two decades, racial and ethnic disparities in tuberculosis outcomes persist. We aimed to examine the extent to which inequalities in health and neighbourhood-level social vulnerability mediate these disparities. METHODS: We extracted data from the US National Tuberculosis Surveillance System on individuals with tuberculosis during 2011-19. Individuals with multidrug-resistant tuberculosis or missing data on race and ethnicity were excluded. We examined potential disparities in tuberculosis outcomes among US-born and non-US-born individuals and conducted a mediation analysis for groups with a higher risk of treatment incompletion (a summary outcome comprising diagnosis after death, treatment discontinuation, or death during treatment). We used sequential multiple mediation to evaluate eight potential mediators: three comorbid conditions (HIV, end-stage renal disease, and diabetes), homelessness, and four census tract-level measures (poverty, unemployment, insurance coverage, and racialised economic segregation [measured by Index of Concentration at the ExtremesRace-Income]). We estimated the marginal contribution of each mediator using Shapley values. FINDINGS: During 2011-19, 27 788 US-born individuals and 57 225 non-US-born individuals were diagnosed with active tuberculosis, of whom 27 605 and 56 253 individuals, respectively, met eligibility criteria for our analyses. We did not observe evidence of disparities in tuberculosis outcomes for non-US-born individuals by race and ethnicity. Therefore, subsequent analyses were restricted to US-born individuals. Relative to White individuals, Black and Hispanic individuals had a higher risk of not completing tuberculosis treatment (adjusted relative risk 1·27, 95% CI 1·19-1·35; 1·22, 1·11-1·33, respectively). In multiple mediator analysis, the eight measured mediators explained 67% of the disparity for Black individuals and 65% for Hispanic individuals. The biggest contributors to these disparities for Black individuals and Hispanic individuals were concomitant end-stage renal disease, concomitant HIV, census tract-level racialised economic segregation, and census tract-level poverty. INTERPRETATION: Our findings underscore the need for initiatives to reduce disparities in tuberculosis outcomes among US-born individuals, particularly in highly racially and economically polarised neighbourhoods. Mitigating the structural and environmental factors that lead to disparities in the prevalence of comorbidities and their case management should be a priority. FUNDING: US Centers for Disease Control and Prevention National Center for HIV, Viral Hepatitis, STD, and Tuberculosis Prevention Epidemiologic and Economic Modeling Agreement.

The long-term effects of domestic and international tuberculosis service improvements on tuberculosis trends within the USA: a mathematical modelling study.

BACKGROUND: For settings with low tuberculosis incidence, disease elimination is a long-term goal. We investigated pathways to tuberculosis pre-elimination (incidence <1·0 cases per 100 000 people) and elimination (incidence <0·1 cases per 100 000 people) in the USA, where incidence was estimated at 2·9 per 100 000 people in 2023. METHODS: Using a mathematical modelling framework, we simulated how US tuberculosis incidence could be affected by changes in tuberculosis services in the countries of origin for future migrants to the USA, as well as changes in tuberculosis services inside the USA. To do so, we used a linked set of transmission dynamic models, calibrated to demographic and epidemiological data for each setting. We constructed intervention scenarios representing improvements in tuberculosis services internationally and within the USA, individually and in combination, plus a base-case scenario representing continuation of current services. We simulated health and economic outcomes until 2100, using a Bayesian approach to quantify uncertainty in these outcomes. FINDINGS: Under the base-case scenario, US tuberculosis incidence was projected to decline to 1·8 cases per 100 000 (95% uncertainty interval [UI] 1·5-2·1) in the total population by 2050. Intervention scenarios produced substantial reductions in tuberculosis incidence, with the combination of all domestic and international interventions projected to achieve pre-elimination by 2033 (95% UI 2031-2037). Compared with the base-case scenario, this combination of interventions could avert 101 000 tuberculosis cases (95% UI 84 000-120 000) and 13 300 tuberculosis deaths (95% UI 10 500-16 300) in the USA from 2025 to 2050. Tuberculosis elimination was not projected before 2100. INTERPRETATION: Strengthening tuberculosis services domestically, promoting the development of more effective technologies and interventions, and supporting tuberculosis programmes in countries with a high tuberculosis burden are key strategies for accelerating progress towards tuberculosis elimination in the USA. FUNDING: US Centers for Disease Control and Prevention.

Modelling the potential impact of global hepatitis B vaccination on the burden of chronic hepatitis B in the United States.

About 80% of persons with chronic hepatitis B virus (HBV) infection in the United States are non-US-born. Despite improvements in infant hepatitis B vaccination globally since 2000, work remains to attain the World Health Organization's (WHO) global 2030 goal of 90% vaccination. We explore the impacts on the United States of global progress in hepatitis B vaccination since 2000 and of achieving WHO hepatitis B vaccination goals. We simulated immigrants with HBV infection arriving to the United States from 2000 to 2070 using models of the 10 countries from which the largest numbers of individuals with HBV infection were born. We estimated costs in the United States among these cohorts using a disease simulation model. We simulated three scenarios: a scenario with no progress in infant vaccination for hepatitis B since 2000 (baseline), current (2020) progress and achieving WHO 2030 goals for hepatitis B vaccination. We estimate current hepatitis B vaccination progress since the 2000 baseline in these 10 countries will lead to 468,686 fewer HBV infections, avoid 35,582 hepatitis B-related deaths and save $4.2 billion in the United States through 2070. Achieving the WHO 2030 90% hepatitis B infant vaccination targets could lead to an additional 16,762 fewer HBV infections, 989 fewer hepatitis B-related deaths and save $143 million through 2070. Global hepatitis B vaccination since 2000 reduced prevalence of HBV infection in the United States. Achieving the WHO 2030 infant vaccination goals globally could lead to over one hundred million dollars in additional savings.

Potential impact of curative and preventive interventions toward hepatitis C elimination in people who inject drugs-A network modeling study.

BACKGROUND: Injection-equipment-sharing networks play an important role in hepatitis C virus (HCV) transmission among people who inject drugs (PWID). Direct-acting antiviral (DAA) treatments for HCV infection and interventions to prevent HCV transmission are critical components of an overall hepatitis C elimination strategy, but how they contribute to the elimination outcomes in different PWID network settings are unclear. METHODS: We developed an agent-based network model of HCV transmission through the sharing of injection equipment among PWID and parameterized and calibrated the model with rural PWID data in the United States. We modeled curative and preventive interventions at annual coverage levels of 12.5 %, 25 %, or 37.5 % (cumulative percentage of eligible individuals engaged), and two allocation approaches: random vs targeting PWID with more injection partners (hereafter 'degree-based'). We compared the impact of these intervention strategies on prevalence and incidence of HCV infections. We conducted sensitivity analysis on key parameters governing the effects of curative and preventive interventions and PWID network characteristics. RESULTS: Combining curative and preventive interventions at 37.5 % annual coverage with degree-based allocation decreased prevalence and incidence of HCV infection by 67 % and 70 % over two years, respectively. Curative interventions decreased prevalence by six to 12 times more than preventive interventions, while curative and preventive interventions had comparable effectiveness on reducing incidence. Intervention impact increased with coverage almost linearly across all intervention strategies, and degree-based allocation was always more effective than random allocation, especially for preventive interventions. Results were sensitive to parameter values defining intervention effects and network mean degree. CONCLUSION: DAA treatments are effective in reducing both prevalence and incidence of HCV infection in PWID, but preventive interventions play a significant role in reducing incidence when intervention coverage is low. Increasing coverage, including efforts in reaching individuals with the most injection partners, preventing reinfection, and improving compliance and retention in preventive services can substantially improve the outcomes. PWID network characteristics should be considered when designing hepatitis C elimination programs.

Assessing thresholds of resistance prevalence at which empiric treatment of gonorrhea should change among men who have sex with men in the US: A cost-effectiveness analysis.

BACKGROUND: Since common diagnostic tests for gonorrhea do not provide information about susceptibility to antibiotics, treatment of gonorrhea remains empiric. Antibiotics used for empiric therapy are usually changed once resistance prevalence exceeds a certain threshold (e.g., 5%). A low switch threshold is intended to increase the probability that an infection is successfully treated with the first-line antibiotic, but it could also increase the pace at which recommendations are switched to newer antibiotics. Little is known about the impact of changing the switch threshold on the incidence of gonorrhea, the rate of treatment failure, and the overall cost and quality-adjusted life-years (QALYs) associated with gonorrhea. METHODS AND FINDINGS: We developed a transmission model of gonococcal infection with multiple resistant strains to project gonorrhea-associated costs and loss in QALYs under different switch thresholds among men who have sex with men (MSM) in the United States. We accounted for the costs and disutilities associated with symptoms, diagnosis, treatment, and sequelae, and combined costs and QALYs in a measure of net health benefit (NHB). Our results suggest that under a scenario where 3 antibiotics are available over the next 50 years (2 suitable for the first-line therapy of gonorrhea and 1 suitable only for the retreatment of resistant infections), changing the switch threshold between 1% and 10% does not meaningfully impact the annual number of gonorrhea cases, total costs, or total QALY losses associated with gonorrhea. However, if a new antibiotic is to become available in the future, choosing a lower switch threshold could improve the population NHB. If in addition, drug-susceptibility testing (DST) is available to inform retreatment regimens after unsuccessful first-line therapy, setting the switch threshold at 1% to 2% is expected to maximize the population NHB. A limitation of our study is that our analysis only focuses on the MSM population and does not consider the influence of interventions such as vaccine and common use of rapid drugs susceptibility tests to inform first-line therapy. CONCLUSIONS: Changing the switch threshold for first-line antibiotics may not substantially change the health and financial outcomes associated with gonorrhea. However, the switch threshold could be reduced when newer antibiotics are expected to become available soon or when in addition to future novel antibiotics, DST is also available to inform retreatment regimens.

Combining genomic data and infection estimates to characterize the complex dynamics of SARS-CoV-2 Omicron variants in the US.

Omicron surged as a variant of concern in late 2021. Several distinct Omicron variants appeared and overtook each other. We combined variant frequencies and infection estimates from a nowcasting model for each US state to estimate variant-specific infections, attack rates, and effective reproduction numbers (Rt). BA.1 rapidly emerged, and we estimate that it infected 47.7% of the US population before it was replaced by BA.2. We estimate that BA.5 infected 35.7% of the US population, persisting in circulation for nearly 6 months. Other variants-BA.2, BA.4, and XBB-together infected 30.7% of the US population. We found a positive correlation between the state-level BA.1 attack rate and social vulnerability and a negative correlation between the BA.1 and BA.2 attack rates. Our findings illustrate the complex interplay between viral evolution, population susceptibility, and social factors during the Omicron emergence in the US.

State-level population estimates of sexual minority adolescents in the United States: A predictive modeling study.

PURPOSE: The Youth Risk Behavior Survey (YRBS) among high school students includes standard questions about sexual identity and sex of sexual contacts, but these questions are not consistently included in every state that conducts the survey. This study aimed to develop and apply a method to predict state-level proportions of high school students identifying as lesbian, gay, or bisexual (LGB) or reporting any same-sex sexual contacts in those states that did not include these questions in their 2017 YRBS. METHODS: We used state-level high school YRBS data from 2013, 2015, and 2017. We defined two primary outcomes relating to self-reported LGB identity and reported same-sex sexual contacts. We developed machine learning models to predict the two outcomes based on other YRBS variables, and comparing different modeling approaches. We used a leave-one-out cross-validation approach and report results from best-performing models. RESULTS: Modern ensemble models outperformed traditional linear models at predicting state-level proportions for the two outcomes, and we identified prediction methods that performed well across different years and prediction tasks. Predicted proportions of respondents reporting LGB identity in states that did not include direct measurement ranged between 9.4% and 12.9%. Predicted proportions of respondents reporting any same-sex contacts, where not directly observed, ranged between 7.0% and 10.4%. CONCLUSION: Comparable population estimates of sexual minority adolescents can raise awareness among state policy makers and the public about what proportion of youth may be exposed to disparate health risks and outcomes associated with sexual minority status. This information can help decision makers in public health and education agencies design, implement and evaluate community and school interventions to improve the health of LGB youth.

Disparities in Tuberculosis Incidence by Race and Ethnicity Among the U.S.-Born Population in the United States, 2011 to 2021 : An Analysis of National Disease Registry Data.

BACKGROUND: Elevated tuberculosis (TB) incidence rates have recently been reported for racial/ethnic minority populations in the United States. Tracking such disparities is important for assessing progress toward national health equity goals and implementing change. OBJECTIVE: To quantify trends in racial/ethnic disparities in TB incidence among U.S.-born persons. DESIGN: Time-series analysis of national TB registry data for 2011 to 2021. SETTING: United States. PARTICIPANTS: U.S.-born persons stratified by race/ethnicity. MEASUREMENTS: TB incidence rates, incidence rate differences, and incidence rate ratios compared with non-Hispanic White persons; excess TB cases (calculated from incidence rate differences); and the index of disparity. Analyses were stratified by sex and by attribution of TB disease to recent transmission and were adjusted for age, year, and state of residence. RESULTS: In analyses of TB incidence rates for each racial/ethnic population compared with non-Hispanic White persons, incidence rate ratios were as high as 14.2 (95% CI, 13.0 to 15.5) among American Indian or Alaska Native (AI/AN) females. Relative disparities were greater for females, younger persons, and TB attributed to recent transmission. Absolute disparities were greater for males. Excess TB cases in 2011 to 2021 represented 69% (CI, 66% to 71%) and 62% (CI, 60% to 64%) of total cases for females and males, respectively. No evidence was found to indicate that incidence rate ratios decreased over time, and most relative disparity measures showed small, statistically nonsignificant increases. LIMITATION: Analyses assumed complete TB case diagnosis and self-report of race/ethnicity and were not adjusted for medical comorbidities or social determinants of health. CONCLUSION: There are persistent disparities in TB incidence by race/ethnicity. Relative disparities were greater for AI/AN persons, females, and younger persons, and absolute disparities were greater for males. Eliminating these disparities could reduce overall TB incidence by more than 60% among the U.S.-born population. PRIMARY FUNDING SOURCE: Centers for Disease Control and Prevention.

Racial and ethnic disparities in diagnosis and treatment outcomes among US-born people diagnosed with tuberculosis, 2003-19: an analysis of national surveillance data.

BACKGROUND: Persistent racial and ethnic disparities in tuberculosis incidence exist in the USA, however, less is known about disparities along the tuberculosis continuum of care. This study aimed to describe how race and ethnicity are associated with tuberculosis diagnosis and treatment outcomes. METHODS: In this analysis of national surveillance data, we extracted data from the US National Tuberculosis Surveillance System on US-born patients with tuberculosis during 2003-19. To estimate the association between race and ethnicity and tuberculosis diagnosis (diagnosis after death, cavitation, and sputum smear positivity) and treatment outcomes (treatment for more than 12 months, treatment discontinuation, and death during treatment), we fitted log-binomial regression models adjusting for calendar year, sex, age category, and regional division. Race and ethnicity were defined based on US Census Bureau classification as White, Black, Hispanic, Asian, American Indian or Alaska Native, Native Hawaiian or Pacific Islander, and people of other ethnicities. We quantified racial and ethnic disparities as adjusted relative risks (aRRs) using non-Hispanic White people as the reference group. We also calculated the Index of Disparity as a summary measure that quantifies the dispersion in a given outcome across all racial and ethnic groups, relative to the population mean. We estimated time trends in each outcome to evaluate whether disparities were closing or widening. FINDINGS: From 2003 to 2019, there were 72 809 US-born individuals diagnosed with tuberculosis disease of whom 72 369 (35·7% women and 64·3% men) could be included in analyses. We observed an overall higher risk of any adverse outcome (defined as diagnosis after death, treatment discontinuation, or death during treatment) for non-Hispanic Black people (aRR 1·27, 95% CI 1·22-1·32), Hispanic people (1·20, 1·14-1·27), and American Indian or Alaska Native people (1·24, 1·12-1·37), relative to non-Hispanic White people. The Index of Disparity for this summary outcome remained unchanged over the study period. INTERPRETATION: This study, based on national surveillance data, indicates racial and ethnic disparaties among US-born tuberculosis patients along the tuberculosis continuum of care. Initiatives are needed to reduce diagnostic delays and improve treatment outcomes for US-born racially marginalised people in the USA. FUNDING: US Centers for Disease Control and Prevention.

Estimated rates of progression to tuberculosis disease for persons infected with Mycobacterium tuberculosis in the United States.

BACKGROUND: In the United States, over 80% of tuberculosis (TB) disease cases are estimated to result from reactivation of latent TB infection (LTBI) acquired more than 2 years previously ("reactivation TB"). We estimated reactivation TB rates for the US population with LTBI, overall, by age, sex, race-ethnicity, and US-born status, and for selected comorbidities (diabetes, end-stage renal disease, and HIV). METHODS: We collated nationally representative data for 2011-2012. Reactivation TB incidence was based on TB cases reported to the National TB Surveillance System that were attributed to LTBI reactivation. Person-years at risk of reactivation TB were calculated using interferon-gamma release assay (IGRA) positivity from the National Health and Nutrition Examination Survey, published values for interferon-gamma release assay sensitivity and specificity, and population estimates from the American Community Survey. RESULTS: For persons aged ≥6 years with LTBI, the overall reactivation rate was estimated as 0.072 (95% uncertainty interval: 0.047, 0.12) per 100 person-years. Estimated reactivation rates declined with age. Compared to the overall population, estimated reactivation rates were higher for persons with diabetes (adjusted rate ratio [aRR] = 1.6 [1.5, 1.7]), end-stage renal disease (aRR = 9.8 [5.4, 19]), and HIV (aRR = 12 [10, 13]). CONCLUSIONS: In our study, individuals with LTBI faced small, non-negligible risks of reactivation TB. Risks were elevated for individuals with medical comorbidities that weaken immune function.

Resource Utilization and Costs Associated with Approaches to Identify Infants with Early-Onset Sepsis.

Objective. To compare resource utilization and costs associated with 3 alternative screening approaches to identify early-onset sepsis (EOS) in infants born at ≥35 wk of gestational age, as recommended by the American Academy of Pediatrics (AAP) in 2018. Study Design. Decision tree-based cost analysis of the 3 AAP-recommended approaches: 1) categorical risk assessment (categorization by chorioamnionitis exposure status), 2) neonatal sepsis calculator (a multivariate prediction model based on perinatal risk factors), and 3) enhanced clinical observation (assessment based on serial clinical examinations). We evaluated resource utilization and direct costs (2022 US dollars) to the health system. Results. Categorical risk assessment led to the greatest neonatal intensive care unit usage (210 d per 1,000 live births) and antibiotic exposure (6.8%) compared with the neonatal sepsis calculator (112 d per 1,000 live births and 3.6%) and enhanced clinical observation (99 d per 1,000 live births and 3.1%). While the per-live birth hospital costs of the 3 approaches were similar-categorical risk assessment cost $1,360, the neonatal sepsis calculator cost $1,317, and enhanced clinical observation cost $1,310-the cost of infants receiving intervention under categorical risk assessment was approximately twice that of the other 2 strategies. Results were robust to variations in data parameters. Conclusion. The neonatal sepsis calculator and enhanced clinical observation approaches may be preferred to categorical risk assessment as they reduce the number of infants receiving intervention and thus antibiotic exposure and associated costs. All 3 approaches have similar costs over all live births, and prior literature has indicated similar health outcomes. Inclusion of downstream effects of antibiotic exposure in the neonatal period should be evaluated within a cost-effectiveness analysis. Highlights: Of the 3 approaches recommended by the American Academy of Pediatrics in 2018 to identify early-onset sepsis in infants born at ≥35 weeks, the categorical risk assessment approach leads to about twice as many infants receiving evaluation to rule out early-onset sepsis compared with the neonatal sepsis calculator and enhanced clinical observation approaches.While the hospital costs of the 3 approaches were similar over the entire population of live births, the neonatal sepsis calculator and enhanced clinical observation approaches reduce antibiotic exposure, neonatal intensive care unit admission, and hospital costs associated with interventions as part of the screening approach compared with the categorical risk assessment approach.

The Impact of Rapid Drug Susceptibility Tests on Gonorrhea Burden and the Life Span of Antibiotic Treatments: A Modeling Study Among Men Who Have Sex With Men in the United States.

Rapid point-of-care tests that diagnose gonococcal infections and identify susceptibility to antibiotics enable individualized treatment. This could improve patient outcomes and slow the emergence and spread of antibiotic resistance. However, little is known about the long-term impact of such diagnostics on the burden of gonorrhea and the effective life span of antibiotics. We used a mathematical model of gonorrhea transmission among men who have sex with men in the United States to project the annual rate of reported gonorrhea cases and the effective life span of ceftriaxone, the recommended antibiotic for first-line treatment of gonorrhea, as well as 2 previously recommended antibiotics, ciprofloxacin and tetracycline, when a rapid drug susceptibility test that estimates susceptibility to ciprofloxacin and tetracycline is available. The use of a rapid drug susceptibility test with ≥50% sensitivity and ≥95% specificity, defined in terms of correct ascertainment of drug susceptibility and nonsusceptibility status, could increase the combined effective life span of ciprofloxacin, tetracycline, and ceftriaxone by at least 2 years over 25 years of simulation. If test specificity is imperfect, however, the increase in the effective life span of antibiotics is accompanied by an increase in the rate of reported gonorrhea cases even under perfect sensitivity.

Bias-Adjusted Predictions of County-Level Vaccination Coverage from the COVID-19 Trends and Impact Survey.

BACKGROUND: The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey. DESIGN: We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up. RESULTS: Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds. LIMITATIONS: We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape. CONCLUSIONS: Our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time scale and geographic scale necessary for proactive decision making. IMPLICATIONS: Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs. HIGHLIGHTS: The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness.The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement.We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey.Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making.

Resistance-minimising strategies for introducing a novel antibiotic for gonorrhoea treatment: a mathematical modelling study.

BACKGROUND: Gonorrhoea is a highly prevalent sexually transmitted infection and an urgent public health concern because of increasing antibiotic resistance in Neisseria gonorrhoeae. Only ceftriaxone remains as the recommended treatment in the USA. With the prospect of new anti-gonococcal antibiotics being approved, we aimed to evaluate how to deploy a new drug to maximise its clinically useful lifespan. METHODS: We used a compartmental model of gonorrhoea transmission in a US population of men who have sex with men (MSM) to compare strategies for introducing a new antibiotic for gonorrhoea treatment. The MSM population was stratified into three sexual activity groups (low, intermediate, and high) characterised by annual rates of partner change. The four introduction strategies tested were: (1) random 50-50 allocation, where each treatment-seeking infected individual had a 50% probability of receiving either drug A (current drug; a ceftriaxone-like antibiotic) or drug B (a new antibiotic), effective at time 0; (2) combination therapy of both the current drug and the new antibiotic; (3) reserve strategy, by which the new antibiotic was held in reserve until the current therapy reached a 5% threshold prevalence of resistance; and (4) gradual switch, or the gradual introduction of the new drug until random 50-50 allocation was reached. The primary outcome of interest was the time until 5% prevalence of resistance to each of the drugs (the new drug and the current ceftriaxone-like antibiotic); sensitivity of the primary outcome to the properties of the new antibiotic, specifically the probability of resistance emergence after treatment and the fitness costs of resistance, was explored. Secondary outcomes included the time to a 1% resistance threshold for each drug, as well as population-level prevalence, mean and range annual incidence, and the cumulative number of incident gonococcal infections. FINDINGS: Under baseline model conditions, a 5% prevalence of resistance to each of drugs A and B was reached within 13·9 years with the reserve strategy, 18·2 years with the gradual switch strategy, 19·2 years with the random 50-50 allocation strategy, and 19·9 years with the combination therapy strategy. The reserve strategy was consistently inferior for mitigating antibiotic resistance under the parameter space explored and was increasingly outperformed by the other strategies as the probability of de novo resistance emergence decreased and as the fitness costs associated with resistance increased. Combination therapy tended to prolong the development of antibiotic resistance and minimise the number of annual gonococcal infections (under baseline model conditions, mean number of incident infections per year 178 641 [range 177 998-181 731] with combination therapy, 180 084 [178 011-184 405] with the reserve strategy). INTERPRETATION: Our study argues for rapid introduction of new anti-gonococcal antibiotics, recognising that the feasibility of each strategy must incorporate cost, safety, and other practical concerns. The analyses should be revisited once robust estimates of key parameters-ie, the likelihood of emergence of resistance and fitness costs of resistance for the new antibiotic-are available. FUNDING: US Centers for Disease Control and Prevention, National Institute of Allergy and Infectious Diseases.

First Do No Harm? Modeling Risks and Benefits of Challenge Trials for Hepatitis C Vaccine Development.

BACKGROUND: In 2019, about 58 million individuals were chronically infected with hepatitis C virus. Some experts have proposed challenge trials for hepatitis C virus vaccine development. METHODS: We modeled incremental infections averted through a challenge approach, under varying assumptions regarding trial duration, number of candidates, and vaccine uptake. We computed the benefit-risk ratio of incremental benefits to risks for challenge versus traditional approaches. We also benchmarked against monetary costs of achieving incremental benefits through treatment. RESULTS: Our base case assumes 3 vaccine candidates, each with an 11% chance of success, corresponding to a 30% probability of successfully developing a vaccine. Given this probability, and assuming a 5-year difference in duration between challenge and traditional trials, a challenge approach would avert an expected 185 000 incremental infections with 20% steady-state uptake compared to a traditional approach and 832 000 with 90% uptake (quality-adjusted life-year benefit-risk ratio, 72 000 & 323 000). It would cost at least $92 million and $416 million, respectively, to obtain equivalent benefits through treatment. BRRs vary considerably across scenarios, depending on input assumptions. CONCLUSIONS: Benefits of a challenge approach increase with more vaccine candidates, faster challenge trials, and greater uptake.

Adaptive metrics for an evolving pandemic: A dynamic approach to area-level COVID-19 risk designations.

Throughout the COVID-19 pandemic, policymakers have proposed risk metrics, such as the CDC Community Levels, to guide local and state decision-making. However, risk metrics have not reliably predicted key outcomes and have often lacked transparency in terms of prioritization of false-positive versus false-negative signals. They have also struggled to maintain relevance over time due to slow and infrequent updates addressing new variants and shifts in vaccine- and infection-induced immunity. We make two contributions to address these weaknesses. We first present a framework to evaluate predictive accuracy based on policy targets related to severe disease and mortality, allowing for explicit preferences toward false-negative versus false-positive signals. This approach allows policymakers to optimize metrics for specific preferences and interventions. Second, we propose a method to update risk thresholds in real time. We show that this adaptive approach to designating areas as "high risk" improves performance over static metrics in predicting 3-wk-ahead mortality and intensive care usage at both state and county levels. We also demonstrate that with our approach, using only new hospital admissions to predict 3-wk-ahead mortality and intensive care usage has performed consistently as well as metrics that also include cases and inpatient bed usage. Our results highlight that a key challenge for COVID-19 risk prediction is the changing relationship between indicators and outcomes of policy interest. Adaptive metrics therefore have a unique advantage in a rapidly evolving pandemic context.

Tabby2: a user-friendly web tool for forecasting state-level TB outcomes in the United States.

BACKGROUND: In the United States, the tuberculosis (TB) disease burden and associated factors vary substantially across states. While public health agencies must choose how to deploy resources to combat TB and latent tuberculosis infection (LTBI), state-level modeling analyses to inform policy decisions have not been widely available. METHODS: We developed a mathematical model of TB epidemiology linked to a web-based user interface - Tabby2. The model is calibrated to epidemiological and demographic data for the United States, each U.S. state, and the District of Columbia. Users can simulate pre-defined scenarios describing approaches to TB prevention and treatment or create their own intervention scenarios. Location-specific results for epidemiological outcomes, service utilization, costs, and cost-effectiveness are reported as downloadable tables and customizable visualizations. To demonstrate the tool's functionality, we projected trends in TB outcomes without additional intervention for all 50 states and the District of Columbia. We further undertook a case study of expanded treatment of LTBI among non-U.S.-born individuals in Massachusetts, covering 10% of the target population annually over 2025-2029. RESULTS: Between 2022 and 2050, TB incidence rates were projected to decline in all states and the District of Columbia. Incidence projections for the year 2050 ranged from 0.03 to 3.8 cases (median 0.95) per 100,000 persons. By 2050, we project that majority (> 50%) of TB will be diagnosed among non-U.S.-born persons in 46 states and the District of Columbia; per state percentages range from 17.4% to 96.7% (median 83.0%). In Massachusetts, expanded testing and treatment for LTBI in this population was projected to reduce cumulative TB cases between 2025 and 2050 by 6.3% and TB-related deaths by 8.4%, relative to base case projections. This intervention had an incremental cost-effectiveness ratio of $180,951 (2020 USD) per quality-adjusted life year gained from the societal perspective. CONCLUSIONS: Tabby2 allows users to estimate the costs, impact, and cost-effectiveness of different TB prevention approaches for multiple geographic areas in the United States. Expanded testing and treatment for LTBI could accelerate declines in TB incidence in the United States, as demonstrated in the Massachusetts case study.

Use of Wastewater Metrics to Track COVID-19 in the US.

Importance: Widespread use of at-home COVID-19 tests hampers determination of community COVID-19 incidence. Objective: To examine the association of county-level wastewater metrics with high case and hospitalization rates nationwide both before and after widespread use of at-home tests. Design, Setting, and Participants: This observational cohort study with a time series analysis was conducted from January to September 2022 in 268 US counties in 22 states participating in the US Centers for Disease Control and Prevention's National Wastewater Surveillance System. Participants included the populations of those US counties. Exposures: County level of circulating SARS-CoV-2 as determined by metrics based on viral wastewater concentration relative to the county maximum (ie, wastewater percentile) and 15-day percentage change in SARS-CoV-2 (ie, percentage change). Main Outcomes and Measures: High county incidence of COVID-19 as evidenced by dichotomized reported cases (current cases ≥200 per 100 000 population) and hospitalization (≥10 per 100 000 population lagged by 2 weeks) rates, stratified by calendar quarter. Results: In the first quarter of 2022, use of the wastewater percentile detected high reported case (area under the curve [AUC], 0.95; 95% CI, 0.94-0.96) and hospitalization (AUC, 0.86; 95% CI, 0.84-0.88) rates. The percentage change metric performed poorly, with AUCs ranging from 0.51 (95% CI, 0.50-0.53) to 0.57 (95% CI, 0.55-0.59) for reported new cases, and from 0.50 (95% CI, 0.48-0.52) to 0.55 (95% CI, 0.53-0.57) for hospitalizations across the first 3 quarters of 2022. The Youden index for detecting high case rates was wastewater percentile of 51% (sensitivity, 0.82; 95% CI, 0.80-0.84; specificity, 0.93; 95% CI, 0.92-0.95). A model inclusive of both metrics performed no better than using wastewater percentile alone. The performance of wastewater percentile declined over time for cases in the second quarter (AUC, 0.84; 95% CI, 0.82-0.86) and third quarter (AUC, 0.72; 95% CI, 0.70-0.75) of 2022. Conclusions and Relevance: In this study, nationwide, county wastewater levels relative to the county maximum were associated with high COVID-19 case and hospitalization rates in the first quarter of 2022, but there was increasing dissociation between wastewater and clinical metrics in subsequent quarters, which may reflect increasing underreporting of cases, reduced testing, and possibly lower virulence of infection due to vaccines and treatments. This study offers a strategy to operationalize county wastewater percentile to improve the accurate assessment of community SARS-CoV-2 infection prevalence when reliability of conventional surveillance data is declining.

Changes in Population Immunity Against Infection and Severe Disease From Severe Acute Respiratory Syndrome Coronavirus 2 Omicron Variants in the United States Between December 2021 and November 2022.

BACKGROUND: Although a substantial fraction of the US population was infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) during December 2021-February 2022, the subsequent evolution of population immunity reflects the competing influences of waning protection over time and acquisition or restoration of immunity through additional infections and vaccinations. METHODS: Using a Bayesian evidence synthesis model of reported coronavirus disease 2019 (COVID-19) data (diagnoses, hospitalizations), vaccinations, and waning patterns for vaccine- and infection-acquired immunity, we estimate population immunity against infection and severe disease from SARS-CoV-2 Omicron variants in the United States, by location (national, state, county) and week. RESULTS: By 9 November 2022, 97% (95%-99%) of the US population were estimated to have prior immunological exposure to SARS-CoV-2. Between 1 December 2021 and 9 November 2022, protection against a new Omicron infection rose from 22% (21%-23%) to 63% (51%-75%) nationally, and protection against an Omicron infection leading to severe disease increased from 61% (59%-64%) to 89% (83%-92%). Increasing first booster uptake to 55% in all states (current US coverage: 34%) and second booster uptake to 22% (current US coverage: 11%) would increase protection against infection by 4.5 percentage points (2.4-7.2) and protection against severe disease by 1.1 percentage points (1.0-1.5). CONCLUSIONS: Effective protection against SARS-CoV-2 infection and severe disease in November 2022 was substantially higher than in December 2021. Despite this high level of protection, a more transmissible or immune evading (sub)variant, changes in behavior, or ongoing waning of immunity could lead to a new SARS-CoV-2 wave.

Gaps in Prenatal Hepatitis B Screening and Management of HBsAg Positive Pregnant Persons in the U.S., 2015-2020.

BACKGROUND: The Advisory Committee for Immunization Practices (ACIP) recommends testing all pregnant women for hepatitis B surface antigen (HBsAg) and testing HBsAg-positive pregnant women for hepatitis B virus deoxyribonucleic acid (HBV DNA). HBsAg-positive pregnant persons are recommended by the American Association for the Study of Liver Diseases to receive regular monitoring, including alanine transaminase (ALT) and HBV DNA and antiviral therapy for active hepatitis and to prevent perinatal HBV transmission if HBV DNA level is >200,000 IU/mL. METHODS: Using Optum Clinformatics Data Mart Database claims data, pregnant women who received HBsAg testing and HBsAg-positive pregnant persons who received HBV DNA and alt testing and antiviral therapy during pregnancy and after delivery during January 1, 2015-December 31, 2020 were analyzed. RESULTS: Among 506,794 pregnancies, 14.6% did not receive HBsAg testing. Pregnant women more likely to receive testing for HBsAg (p<0.01) were persons aged ≥20 years, were Asian, had >1 child, or received education beyond high school. Among the 0.28% (1,437) pregnant women who tested positive for hepatitis B surface antigen, 46% were Asian. The proportion of HBsAg-positive pregnant women who received HBV DNA testing during pregnancy and in the 12 months after delivery was 44.3% and 28.6%, respectively; the proportion that received hepatitis B e antigen was 31.6% and 12.7%, respectively; the proportion that received ALT testing was 67.4% and 47%, respectively; and the proportion that received HBV antiviral therapy was 7% and 6.2%, respectively. CONCLUSIONS: This study suggests that as many as half a million (∼14%) pregnant persons who gave birth each year were not tested for HBsAg to prevent perinatal transmission. More than 50% of HBsAg-positive persons did not receive the recommended HBV-directed monitoring tests during pregnancy and after delivery.