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Immune checkpoint blockade therapy targets T cell-negative costimulatory molecules such as cytotoxic T lymphocyte antigen-4 (CTLA-4) and programmed cell death-1 (PD-1). Combination anti–CTLA-4 and anti–PD-1 blockade therapy has enhanced efficacy, but it remains unclear through what mechanisms such effects are mediated. A critical question is whether combination therapy targets and modulates the same T cell populations as monotherapies. Using a mass cytometry-based systems approach, we comprehensively profiled the response of T cell populations to monotherapy and combination anti–CTLA-4 plus anti–PD-1 therapy in syngeneic murine tumors and clinical samples. Most effects of monotherapies were additive in the context of combination therapy; however, multiple combination therapy-specific effects were observed. Highly phenotypically exhausted cluster of differentiation 8 (CD8) T cells expand in frequency following anti–PD-1 monotherapy but not combination therapy, while activated terminally differentiated effector CD8 T cells expand only following combination therapy. Combination therapy also led to further increased frequency of T helper type 1 (Th1)-like CD4 effector T cells even though anti–PD-1 monotherapy is not sufficient to do so. Mass cytometry analyses of peripheral blood from melanoma patients treated with immune checkpoint blockade therapies similarly revealed mostly additive effects on the frequencies of T cell subsets along with unique modulation of terminally differentiated effector CD8 T cells by combination ipilimumab plus nivolumab therapy. Together, these findings indicate that dual blockade of CTLA-4 and PD-1 therapy is sufficient to induce unique cellular responses compared with either monotherapy.

U.S. military Service members have consistently smoked more than the general population and the prevalence of smoking is even higher among U.S. veterans. Our study examined cigarette smoking patterns among Service members before and after military separation to better understand the disproportionate rate of smoking among veterans. Data from the Millennium Cohort Study were used. All study participants were in the military at baseline and some transitioned from the military to civilian life during the observation period. We investigated any impact of military separation on smoking, as well as other potential risk factors for smoking. Overall, we observed higher smoking prevalence among veterans than Service members. Additionally, we found that Service members smoked more while approaching their separation from the military. Longitudinal analysis revealed military separation was not a risk factor for smoking, as we had hypothesized. Baseline smoking was the most influential predictor of current smoking status. Other significant factors included alcohol consumption, life stressors, and mental health conditions, among others. Military separation was not a risk factor for smoking. However, Service members in the process of transitioning out of the military, as well as high alcohol consumers and Service members with mental health conditions, may be at higher risk of smoking. Including smoking prevention/cessation programs in pre-separation counseling sessions and developing smoking screening and cessation programs targeting specific high-risk subgroups may reduce smoking among Service members and veterans.

Tuberculosis (TB) has a well-established association with military populations, but the association of increased TB risk during armed conflict is less certain. This historical review focuses on the evolution of screening practices, the changing epidemiology of TB, and the risk of TB among US military service members during armed conflict from 1885 to the present. Overall, deployed soldiers were not at increased risk for TB compared with nondeployed soldiers in any of these conflicts, and the risk of TB in the US military largely reflected that of the underlying US population. Nevertheless, there are focal risk groups with higher rates of TB in the military, including prisoners of war. Although the principles of TB control in the military conform to those used in the civilian population, unique military exposures during both times of peace and of armed conflict require additional screening, surveillance, and control measures.

Influenza viruses remain a global threat with the potential to trigger outbreaks and pandemics. Globally, seasonal influenza viruses' mortality range from 291 243-645 832 annually, of which 17% occurs in Sub-Saharan Africa. We sought to estimate the overall prevalence of influenza infections in Kenya, identifying factors influencing the distribution of these infections, and describe trends in occurrence from 2007 to 2013. Surveillance was conducted at eight district hospital sites countrywide. Participants who met the case definition for influenza-like illness were enrolled in the surveillance program. The nasopharyngeal specimens were collected from all participants. We tested all specimens for influenza viruses with quantitative reverse transcriptase real-time polymerase chain reaction (RT-qPCR) assay. Bivariate and multivariate log-binomial regression was performed with a statistically significant level of p<0.005. An administrative map of Kenya was used to locate the geographical distribution of surveillance sites in counties. We visualized the monthly trend of influenza viruses with a graph and chart using exponential smoothing at a damping factor of 0.5 over the study period (2007-2013). A total of 17446 participants enrolled in the program. The overall prevalence of influenza viruses was 19% (n = 3230), of which 76% (n = 2449) were type A, 21% (n = 669) type B and 3% (n = 112) A/ B coinfection. Of those with type A, 59% (n = 1451) were not subtyped. Seasonal influenza A/H3N2 was found in 48% (n = 475), influenza A/H1N1/pdm 2009 in 43% (n = 434), and seasonal influenza A/ H1N1 in 9% (n = 88) participants. Both genders were represented, whereas a large proportion of participants 55% were [less than or equal to]1year age. Influenza prevalence was high, 2 times more in other age categories compared to [less than or equal to]1year age. Category of occupation other than children and school attendees had a high prevalence of influenza virus (p< <0.001). The monthly trends of influenza viruses' positivity showed no seasonal pattern. Influenza types A and B co-circulated throughout the annual calendar during seven years of the surveillance. Influenza viruses circulate year-round and occur among children as well as the adult population in Kenya. Occupational and school-based settings showed a higher prevalence of influenza viruses. There were no regular seasonal patterns for influenza viruses.

Background In the metabolic disease area, there has been a long-time debate against using mixed models for repeated measures (MMRM) as the primary analysis of longitudinal continuous endpoints. As missing data arising from missing not at random assumptions are not addressed in MMRM, multiple imputation based on specific assumptions has been brought into play. Among many missing not at random assumptions with varying degrees of conservativeness, multiple imputation based on retrieved dropouts (MIRD) has been accepted by regulatory agencies in several type 2 diabetes and chronic weight management products in recent years, marking the beginning of a new standard for analysis of longitudinal data in this disease area. Methods On top of the established MIRD approach of which the imputation is based on last on-treatment data of retrieved dropout (RD)s, we propose a new class of MIRD approaches utilizing all available data from RDs. The imputation implementation can be one-step Markov Chain Monte Carlo (MCMC) or two-step (creating monotone missingness, followed by regression approach). ANCOVA can be applied to the complete dataset post imputation and Rubin’s rule can be used to combine all estimates into a single estimate. Simulation studies in a wide range of scenarios are conducted to understand the type-I error and power rates of the new class versus the established MIRD approach and other reference statistical methods such as MMRM. Results Overall, the new class has very similar performance compared to the established MIRD approach based on last on-treatment data. What’s more interesting is the one-step MCMC approach has better controlled type-I error and is more powerful than the established MIRD approach in certain scenarios with the difference gradually diminishing with larger sample size. The data analyses based on two real phase 3 datasets further manifest the power conclusions, with the results based on the new class applied to the larger of the two datasets almost identical to that of on-study MMRM. Conclusions We have presented comprehensive implementations of the MIRD approach for continuous endpoints in a longitudinal setting that fully fit within the strategy of treatment policy. The proposed new class based on all observed data of RDs is proved to be as powerful as the established MIRD approach based on last on-treatment visit in most scenarios. The one-step MCMC approach is more powerful than the established MIRD approach in certain scenarios. Since the new class involves less programming derivation of additional flags, it’s anticipated to be more easily implemented in clinical trial reporting.

Objectives. To assess the effect of the vaccination mandate on COVID-19 vaccination rates and identify independent factors associated with lack of postmandate vaccination among service members. Methods. We assessed all active component service members for COVID-19 vaccination status from December 11, 2020, to January 1, 2022. We used comparative interrupted time series analysis and logistic regression to compare pre- and postmandate completion of the vaccine series between the US military and the US general population. Results. Previous documented infection with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), lower rank, and non-Hispanic Black race were associated with lower premandate vaccination. Postmandate vaccination rates were significantly higher in the active component population ( P < .001) compared with the premandate period and the US population. Also notable was the higher incidence of postmandate vaccination among those who were non-Hispanic Black or of lower rank. Conclusions. The US military’s COVID-19 vaccination mandate was effective at both increasing overall vaccination rates and reducing disparities in vaccination, including race and ethnicity and rank. Vaccine mandates increase the receipt of vaccines and promote health, readiness, and equity within the US military. ( Am J Public Health. 2025;115(7):1146–1156. https://doi.org/10.2105/AJPH.2025.308120 )

We have explored several statistical approaches to impute missing time-to-event data that arise from outcome trials with relatively long follow-up periods. Aligning with the primary estimand, such analyses evaluate the robustness of results by imposing an assumption different from censoring at random (CAR). Although there have been debates over which assumption and which method is more appropriate to be applied to the imputation, we propose to use the collection of retrieved dropouts as the basis of missing data imputation. As retrieved dropouts share a similar disposition, such as treatment discontinuation, with subjects who have missing data, they can reasonably be assumed to characterize the distribution of time-to-event among subjects with missing data. In terms of computational intensity and robustness to violation of underlying distributional assumption, we have compared parametric approaches via MCMC or MLE multivariate sampling procedures to a non-parametric bootstrap approach with respect to baseline hazard function. Each of these approaches follows a process of multiple imputation (“proper imputations”), analysis of complete datasets, and final combination. The type-I error, and power rates are examined under a wide range of scenarios to inform the performance characteristics. A subset of a real unblinded phase III CVOT is used to demonstrate the application of the proposed approaches, compared to the Cox proportional hazards model and jump-to-reference multiple imputation.

Individuals with latent tuberculosis infection (LTBI) represent a reservoir of infection, many of whom will progress to tuberculosis (TB) disease. A central pillar of TB control in the United States is reducing this reservoir through targeted testing and treatment. To estimate the prevalence of LTBI in the United States using the tuberculin skin test (TST) and an IFN-γ release assay. We used nationally representative data from the 2011-2012 National Health and Nutrition Examination Survey (n = 6,083 aged ≥6 yr). LTBI was measured by both the TST and QuantiFERON-TB Gold In-Tube test (QFT-GIT). Weighted population, prevalence, and multiple logistic regression were used. The estimated prevalence of LTBI in 2011-2012 was 4.4% as measured by the TST and 4.8% by QFT-GIT, corresponding to 12,398,000 and 13,628,000 individuals, respectively. Prevalence declined slightly since 2000 among the U.S. born but remained constant among the foreign born. Earlier birth cohorts consistently had higher prevalence than more recent ones. Higher risk groups included the foreign born, close contact with a case of TB disease, and certain racial/ethnic groups. After years of decline, the prevalence of LTBI remained relatively constant between 2000 and 2011. A large reservoir of 12.4 million still exists, with foreign-born persons representing an increasingly larger proportion of this reservoir (73%). Estimates and risk factors for LTBI were generally similar between the TST and QFT-GIT. The updated estimates of LTBI and associated risk groups can help improve targeted testing and treatment in the United States.

Viruses are responsible for a large proportion of acute respiratory tract infections (ARTIs). Human influenza, parainfluenza, respiratory-syncytial-virus, and adenoviruses are among the leading cause of ARTIs. Epidemiological evidence of those respiratory viruses is limited in the East Africa Community (EAC) region. This review sought to identify the prevalence of respiratory syncytial virus, parainfluenza, and adenoviruses among cases of ARTI in the EAC from 2007 to 2020. A literature search was conducted in Medline, Global Index Medicus, and the grey literature from public health institutions and programs in the EAC. Two independent reviewers performed data extraction. We used a random effects model to pool the prevalence estimate across studies. We assessed heterogeneity with the I2 statistic, and Cochran's Q test, and further we did subgroup analysis. This review was registered with PROSPERO under registration number CRD42018110186. A total of 12 studies met the eligibility criteria for the studies documented from 2007 to 2020. The overall pooled prevalence of adenoviruses was 13% (95% confidence interval [CI]: 6-21, N = 28829), respiratory syncytial virus 11% (95% CI: 7-15, N = 22627), and parainfluenza was 9% (95% CI: 7-11, N = 28363). Pooled prevalence of reported ARTIs, all ages, and locality varied in the included studies. Studies among participants with severe acute respiratory disease had a higher pooled prevalence of all the three viruses. Considerable heterogeneity was noted overall and in subgroup analysis. Our findings indicate that human adenoviruses, respiratory syncytial virus and parainfluenza virus are prevalent in Kenya, Tanzania, and Uganda. These three respiratory viruses contribute substantially to ARTIs in the EAC, particularly among those with severe disease and those aged five and above.

Drug susceptibility testing for Mycobacterium tuberculosis (MTB) is difficult to perform in resource-limited settings where Acid Fast Bacilli (AFB) smears are commonly used for disease diagnosis and monitoring. We developed a simple method for extraction of MTB DNA from AFB smears for sequencing-based detection of mutations associated with resistance to all first and several second-line anti-tuberculosis drugs. We isolated MTB DNA by boiling smear content in a Chelex solution, followed by column purification. We sequenced PCR-amplified segments of the rpoB, katG, embB, gyrA, gyrB, rpsL, and rrs genes, the inhA, eis, and pncA promoters and the entire pncA gene. We tested our assay on 1,208 clinically obtained AFB smears from Ghana (n = 379), Kenya (n = 517), Uganda (n = 262), and Zambia (n = 50). Coverage depth varied by target and slide smear grade, ranging from 300X to 12000X on average. Coverage of ≥20X was obtained for all targets in 870 (72%) slides overall. Mono-resistance (5.9%), multi-drug resistance (1.8%), and poly-resistance (2.4%) mutation profiles were detected in 10% of slides overall, and in over 32% of retreatment and follow-up cases. This rapid AFB smear DNA-based method for determining drug resistance may be useful for the diagnosis and surveillance of drug-resistant tuberculosis.