The Burden of Cancer and Pre-cancerous Conditions Among Transgender Individuals in a Large Healthcare Network.

The current study aimed to examine the prevalence of and risk factors for cancer and pre-cancerous conditions, comparing transgender and cisgender individuals, using 2012-2023 electronic health record data from a large healthcare system. We identified 2,745 transgender individuals using a previously validated computable phenotype and 54,900 matched cisgender individuals. We calculated the prevalence of cancer and pre-cancer related to human papillomavirus (HPV), human immunodeficiency virus (HIV), tobacco, alcohol, lung, breast, colorectum, and built multivariable logistic models to examine the association between gender identity and the presence of cancer or pre-cancer. Results indicated similar odds of developing cancer across gender identities, but transgender individuals exhibited significantly higher risks for pre-cancerous conditions, including alcohol-related, breast, and colorectal pre-cancers compared to cisgender women, and HPV-related, tobacco-related, alcohol-related, and colorectal pre-cancers compared to cisgender men. These findings underscore the need for tailored interventions and policies addressing cancer health disparities affecting the transgender population.

Real-World Effectiveness of Lung Cancer Screening Using Deep Learning-Based Counterfactual Prediction.

The benefits and harms of lung cancer screening (LCS) for patients in the real-world clinical setting have been argued. Recently, discriminative prediction modeling of lung cancer with stratified risk factors has been developed to investigate the real-world effectiveness of LCS from observational data. However, most of these studies were conducted at the population level that only measured the difference in the average outcome between groups. In this study, we built counterfactual prediction models for lung cancer risk and mortality and examined for individual patients whether LCS as a hypothetical intervention reduces lung cancer risk and subsequent mortality. We investigated traditional and deep learning (DL)-based causal methods that provide individualized treatment effect (ITE) at the patient level and evaluated them with a cohort from the OneFlorida+ Clinical Research Consortium. We further discussed and demonstrated that the ITE estimation model can be used to personalize clinical decision support for a broader population.

Efficacy, Convenience, Safety and Durability of DTG-Based Antiretroviral Therapies: Evidence from a Prospective Study by the Italian MaSTER Cohort.

Background: Dolutegravir (DTG) is recommended by international guidelines as a main component of an optimal initial regimen of cART (combination antiretroviral treatment) in people living with HIV (PLWH) and in case of switching for failure or optimization strategies. However, studies on the performance of DTG-containing regimens and indications for switching therapies in the long term are sparse. The purpose of this study was to evaluate prospectively the performance of DTG-based regimens, using the metrics of "efficacy", "safety", "convenience" and ''durability'', among a nationally representative cohort of PLWH in Italy. Methods: We selected all PLWH in four centers of the MaSTER cohort who initiated a DTG-based regimen either when naïve or following a regimen switch between 11 July 2018 and 2 July 2021. Participants were followed until the outcomes were recorded or until the end of the study on 4 August 2022, whichever occurred first. Interruption was reported even when a participant switched to another DTG-containing regimen. Survival regression models were fitted to evaluate associations between therapy performance and age, sex, nationality, risk of HIV transmission, HIV RNA suppression status, CD4+ T-cell count, year of HIV diagnosis, cART status (naïve or experienced), cART backbone and viral hepatitis coinfection. Results: There were 371 participants in our cohort who initiated a DTG-based cART regimen in the time frame of the study. The population was predominantly male (75.2%), of Italian nationality (83.3%), with a history of cART use (80.9%), and the majority initiated a DTG-based regimen following a switch strategy in 2019 (80.1%). Median age was 53 years (interquartile range (IQR): 45-58). Prior cART regimen was based mostly on a combination of NRTI drugs plus a PI-boosted drug (34.2%), followed by a combination of NRTIs plus an NNRTI (23.5%). Concerning the NRTI backbone, the majority comprised 3TC plus ABC (34.5%), followed by 3TC alone (28.6%). The most reported transmission risk factor was heterosexual intercourse (44.2%). Total interruptions of the first DTG-based regimen were registered in 58 (15.6%) participants. The most frequent reason for interruption was due to cART simplification strategies, which accounted for 52%. Only 1 death was reported during the study period. The median time of total follow-up was 556 days (IQR: 316.5-722.5). Risk factors for poor performance of DTG-containing-regimens were found to be: a backbone regimen containing tenofovir, being cART naïve, having detectable HIV RNA at baseline, FIB-4 score above 3.25 and having a cancer diagnosis. By contrast, protective factors were found to be: higher CD4+ T-cell counts and higher CD4/CD8 ratio at baseline. Conclusion: DTG-based regimens were used mainly as a switching therapy in our cohort of PLWH who had undetectable HIV RNA and a good immune status. In this type of population, the durability of DTG-based regimens was maintained in 84.4% of participants with a modest incidence of interruptions mostly due to cART simplification strategies. The results of this prospective real-life study confirm the apparent low risk of changing DTG-containing regimens due to virological failure. They may also help physicians to identify people with increased risk of interruption for different reasons, suggesting targeted medical interventions.

Simulating Colorectal Cancer Trials Using Real-World Data.

PURPOSE: Using real-world data (RWD)-based trial simulation approach, we aim to simulate colorectal cancer (CRC) trials and examine both effectiveness and safety end points in different simulation scenarios. METHODS: We identified five phase III trials comparing new treatment regimens with an US Food and Drug Administration-approved first-line treatment in patients with metastatic CRC (ie, fluorouracil, leucovorin, and irinotecan) as the standard-of-care (SOC) control arm. Using Electronic Health Record-derived data from the OneFlorida network, we defined the study populations and outcome measures using the protocols from the original trials. Our design scenarios were (1) simulation of the SOC fluorouracil, leucovorin, and irinotecan arm and (2) comparative effectiveness research (CER) simulation of the control and experimental arms. For each scenario, we adjusted for random assignment, sampling, and dropout. We used overall survival (OS) and severe adverse events (SAEs) to measure effectiveness and safety. RESULTS: We conducted CER simulations for two trials, and SOC simulations for three trials. The effect sizes of our simulated trials were stable across all simulation runs. Compared with the original trials, we observed longer OS and higher mean number of SAEs in both CER and SOC simulation. In the two CER simulations, hazard ratios associated with death from simulations were similar to that reported in the original trials. Consistent with the original trials, we found higher risk ratios of SAEs in the experiment arm, suggesting potentially higher toxicities from the new treatment regimen. We also observed similar SAE rates across all simulations compared with the original trials. CONCLUSION: In this study, we simulated five CRC trials, and tested two simulation scenarios with several different configurations demonstrated that our simulations can robustly generate effectiveness and safety outcomes comparable with the original trials using real-world data.

A scoping review of semantic integration of health data and information.

OBJECTIVE: We summarized a decade of new research focusing on semantic data integration (SDI) since 2009, and we aim to: (1) summarize the state-of-art approaches on integrating health data and information; and (2) identify the main gaps and challenges of integrating health data and information from multiple levels and domains. MATERIALS AND METHODS: We used PubMed as our focus is applications of SDI in biomedical domains and followed the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) to search and report for relevant studies published between January 1, 2009 and December 31, 2021. We used Covidence-a systematic review management system-to carry out this scoping review. RESULTS: The initial search from PubMed resulted in 5,326 articles using the two sets of keywords. We then removed 44 duplicates and 5,282 articles were retained for abstract screening. After abstract screening, we included 246 articles for full-text screening, among which 87 articles were deemed eligible for full-text extraction. We summarized the 87 articles from four aspects: (1) methods for the global schema; (2) data integration strategies (i.e., federated system vs. data warehousing); (3) the sources of the data; and (4) downstream applications. CONCLUSION: SDI approach can effectively resolve the semantic heterogeneities across different data sources. We identified two key gaps and challenges in existing SDI studies that (1) many of the existing SDI studies used data from only single-level data sources (e.g., integrating individual-level patient records from different hospital systems), and (2) documentation of the data integration processes is sparse, threatening the reproducibility of SDI studies.

Identification of Social and Racial Disparities in Risk of HIV Infection in Florida using Causal AI Methods.

Florida -the 3rd most populous state in the USA-has the highest rates of Human Immunodeficiency Virus (HIV) infections and of unfavorable HIV outcomes, with marked social and racial disparities. In this work, we leveraged large-scale, real-world data, i.e., statewide surveillance records and publicly available data resources encoding social determinants of health (SDoH), to identify social and racial disparities contributing to individuals' risk of HIV infection. We used the Florida Department of Health's Syndromic Tracking and Reporting System (STARS) database (including 100,000+ individuals screened for HIV infection and their partners), and a novel algorithmic fairness assessment method -the Fairness-Aware Causal paThs decompoSition (FACTS)- merging causal inference and artificial intelligence. FACTS deconstructs disparities based on SDoH and individuals' characteristics, and can discover novel mechanisms of inequity, quantifying to what extent they could be reduced by interventions. We paired the deidentified demographic information (age, gender, drug use) of 44,350 individuals in STARS -with non-missing data on interview year, county of residence, and infection status- to eight SDoH, including access to healthcare facilities, % uninsured, median household income, and violent crime rate. Using an expert-reviewed causal graph, we found that the risk of HIV infection for African Americans was higher than for non- African Americans (both in terms of direct and total effect), although a null effect could not be ruled out. FACTS identified several paths leading to racial disparity in HIV risk, including multiple SDoH: education, income, violent crime, drinking, smoking, and rurality.

Prevalence and hospital charges from firearm injuries treated in US emergency departments from 2006 to 2016.

BACKGROUND: Age- and intent-related differences in the burden and costs of firearm injury treated in emergency departments are not well-documented. METHODS: We performed a serial cross-sectional study of the Healthcare Cost and Utilization Program Nationwide Emergency Department Survey from 2006 to 2016. We used International Classification of Diseases diagnoses codes revisions 9 and 10 to identify firearm injuries. We calculated survey-weighted counts, proportions, means, and rates and confidence intervals of national, age-specific (0-4, 5-9, 10-14, 15-17, 18-44, 45-64, 65-84, >84) and intent-specific (assault, unintentional, suicide, undetermined) emergency department discharges for firearm injuries. We used survey-weighted regression to assess temporal trends. RESULTS: There was a total of 868,483 (25.5 per 100,000) emergency department visits for firearm injuries from 2006 to 2016, and 7.8% died in the emergency department. Overall, firearm injury rates remained steady (P = .78). The largest burden was among those 25 to 44 years of age, but their rates remained stable (10.8 per 100,000). Overall assault injuries declined from 39.7% to 36.4%, and overall unintentional injuries increased from 46.4% to 54.7%. Legal-intervention injuries declined from 0.6 to 0.3 per 100,000. The charges (total $4,059,070,364, $369,006,396/year) increased across time in age and intent groups. The mean predicted charges increased from $1,922 to $3,348 in those alive versus $3,741 to $6,515 among those who died. CONCLUSION: Interventions and programs to manage the consequences of firearm injury in persons who live with ongoing morbidity and economic burden are warranted.

An ontology-based documentation of data discovery and integration process in cancer outcomes research.

BACKGROUND: To reduce cancer mortality and improve cancer outcomes, it is critical to understand the various cancer risk factors (RFs) across different domains (e.g., genetic, environmental, and behavioral risk factors) and levels (e.g., individual, interpersonal, and community levels). However, prior research on RFs of cancer outcomes, has primarily focused on individual level RFs due to the lack of integrated datasets that contain multi-level, multi-domain RFs. Further, the lack of a consensus and proper guidance on systematically identify RFs also increase the difficulty of RF selection from heterogenous data sources in a multi-level integrative data analysis (mIDA) study. More importantly, as mIDA studies require integrating heterogenous data sources, the data integration processes in the limited number of existing mIDA studies are inconsistently performed and poorly documented, and thus threatening transparency and reproducibility. METHODS: Informed by the National Institute on Minority Health and Health Disparities (NIMHD) research framework, we (1) reviewed existing reporting guidelines from the Enhancing the QUAlity and Transparency Of health Research (EQUATOR) network and (2) developed a theory-driven reporting guideline to guide the RF variable selection, data source selection, and data integration process. Then, we developed an ontology to standardize the documentation of the RF selection and data integration process in mIDA studies. RESULTS: We summarized the review results and created a reporting guideline-ATTEST-for reporting the variable selection and data source selection and integration process. We provided an ATTEST check list to help researchers to annotate and clearly document each step of their mIDA studies to ensure the transparency and reproducibility. We used the ATTEST to report two mIDA case studies and further transformed annotation results into sematic triples, so that the relationships among variables, data sources and integration processes are explicitly standardized and modeled using the classes and properties from OD-ATTEST. CONCLUSION: Our ontology-based reporting guideline solves some key challenges in current mIDA studies for cancer outcomes research, through providing (1) a theory-driven guidance for multi-level and multi-domain RF variable and data source selection; and (2) a standardized documentation of the data selection and integration processes powered by an ontology, thus a way to enable sharing of mIDA study reports among researchers.

Assessing the effect of data integration on predictive ability of cancer survival models.

Cancer is the second leading cause of death in the United States. To improve cancer prognosis and survival rates, a better understanding of multi-level contributory factors associated with cancer survival is needed. However, prior research on cancer survival has primarily focused on factors from the individual level due to limited availability of integrated datasets. In this study, we sought to examine how data integration impacts the performance of cancer survival prediction models. We linked data from four different sources and evaluated the performance of Cox proportional hazard models for breast, lung, and colorectal cancers under three common data integration scenarios. We showed that adding additional contextual-level predictors to survival models through linking multiple datasets improved model fit and performance. We also showed that different representations of the same variable or concept have differential impacts on model performance. When building statistical models for cancer outcomes, it is important to consider cross-level predictor interactions.

Mining Twitter to assess the determinants of health behavior toward human papillomavirus vaccination in the United States.

OBJECTIVES: The study sought to test the feasibility of using Twitter data to assess determinants of consumers' health behavior toward human papillomavirus (HPV) vaccination informed by the Integrated Behavior Model (IBM). MATERIALS AND METHODS: We used 3 Twitter datasets spanning from 2014 to 2018. We preprocessed and geocoded the tweets, and then built a rule-based model that classified each tweet into either promotional information or consumers' discussions. We applied topic modeling to discover major themes and subsequently explored the associations between the topics learned from consumers' discussions and the responses of HPV-related questions in the Health Information National Trends Survey (HINTS). RESULTS: We collected 2 846 495 tweets and analyzed 335 681 geocoded tweets. Through topic modeling, we identified 122 high-quality topics. The most discussed consumer topic is "cervical cancer screening"; while in promotional tweets, the most popular topic is to increase awareness of "HPV causes cancer." A total of 87 of the 122 topics are correlated between promotional information and consumers' discussions. Guided by IBM, we examined the alignment between our Twitter findings and the results obtained from HINTS. Thirty-five topics can be mapped to HINTS questions by keywords, 112 topics can be mapped to IBM constructs, and 45 topics have statistically significant correlations with HINTS responses in terms of geographic distributions. CONCLUSIONS: Mining Twitter to assess consumers' health behaviors can not only obtain results comparable to surveys, but also yield additional insights via a theory-driven approach. Limitations exist; nevertheless, these encouraging results impel us to develop innovative ways of leveraging social media in the changing health communication landscape.

Cost-Effectiveness of a Statewide Pre-Exposure Prophylaxis Program for Gay, Bisexual, and Other Men Who Have Sex with Men.

Pre-exposure prophylaxis (PrEP) is an effective tool for preventing HIV infection among men who have sex with men (MSM), but its cost-effectiveness has varied across settings. Using an agent-based model, we projected the cost-effectiveness of a statewide PrEP program for MSM in Rhode Island over the next decade. In the absence of PrEP, the model predicted an average of 830 new HIV infections over ten years. Scaling up the existing PrEP program to cover 15% of MSM with ten or more partners each year could reduce the number of new HIV infections by 33.1% at a cost of $184,234 per quality-adjusted life-year (QALY) gained. Expanded PrEP use among MSM at high risk for HIV infection has the potential to prevent a large number of new HIV infections but the high drug-related costs may limit the cost-effectiveness of this intervention.

An Integrated Approach for Efficient Multi-Omics Joint Analysis.

The challenges associated with multi-omics analysis, e.g. DNA-seq, RNA-seq, metabolomics, methylomics and microbiomics domains, include: (1) increased high-dimensionality, as all -omics domains include ten thousands to hundreds of thousands of variables each; (2) increased complexity in analyzing domain-domain interactions, quadratic for pairwise correlation, and exponential for higher-order interactions; (3) variable heterogeneity, with highly skewed distributions in different units and scales for methylation and microbiome. Here, we developed an efficient strategy for joint-domain analysis, applying it to an analysis of correlations between colon epithelium methylomics and fecal microbiomics data with colorectal cancer risk as estimated by colorectal polyp prevalence. First, we applied domain-specific standard pipelines for quality assessment, cleaning, batch-effect removal, et cetera. Second, we performed variable homogenization for both the methylation and microbiome data sets, using domain-specific normalization and dimension reduction, obtaining scale-free variables that could be compared across the two domains. Finally, we implemented a joint-domain network analysis to identify relevant microbial-methylation island patterns. The network analysis considered all possible species-island pairs, thus being quadratic in its complexity. However, we were able to pre-select the unpaired variables by performing a preliminary association analysis on the outcome polyp prevalence. All results from association and interaction analyses were adjusted for multiple comparisons. Although the limited sample size did not provide good power (80% to detect medium to large effect sizes with 5% alpha error), a number of potentially significant association (dozens in the uncorrected analysis, reducing to just a few in the corrected one) were identified As a last step, we linked the network patterns identified by our approach to the KEGG functional ontology, showing that the method can generate new mechanistic hypotheses for the biological causes of polyp development.

Association of statin use with risk of Gleason score-specific prostate cancer: A hospital-based cohort study.

BACKGROUND: Conflicting evidence suggests that statins act chemopreventively against prostate cancer (PCa). Whether the association of statin use with PCa risk is Gleason score-dependent, time-, dose-respondent is not well studied. METHODS: We conducted a cohort study at a tertiary hospital in the Southeastern US using longitudinal data of electronic medical records (EMR) from 1994 to 2016. Only cancer-free men aged >18 years at baseline with follow-up time of ≥12 months were included. Time-dependent Cox proportional hazards regression was used to estimate adjusted hazard ratios (aHRs) with 95% confidence intervals (CIs). RESULTS: Among 13 065 men, 2976 were diagnosed with PCa over median follow-up of 6.6 years. Statin use was associated with lower risk of both Gleason low- (score <7: aHR, 0.85; 95% CI, 0.74-0.96) and high-grade PCa (score ≥7: aHR, 0.54; 95% CI, 0.42-0.69). The protective association was observed only when statins had been used for a relatively longer duration (≥11 months) or higher dose (≥121 defined daily doses), and were more pronounced for PCa of higher Gleason score (<7: aHR, 0.85, 95% CI, 0.74-0.96; 7 [3 + 4]: aHR, 0.62, 95% CI, 0.43-0.90; 7 [4 + 3]: aHR, 0.49, 95% CI, 0.29-0.82; 8: aHR, 0.60, 95% CI, 0.37-0.96; 9-10: aHR, 0.24, 95% CI, 0.11-0.54). Lipophilic statins (aHR, 0.83; 95% CI, 0.72-0.95) might be more protective than hydrophilic statins (aHR, 0.91, 95% CI, 0.63-1.33) against PCa. CONCLUSION: Statin use might be associated with reduced PCa risk only when used for a relatively longer duration, and the risk reduction was higher for PCa of higher Gleason score.

Association of Long-Term Dynamics in Circulating Testosterone with Serum PSA in Prostate Cancer-Free Men with Initial-PSA < 4 ng/mL.

We previously reported that an accelerated decline in circulating testosterone level is associated with a higher risk of prostate cancer (PCa). This study is to examine whether testosterone change rate is related to serum prostate-specific antigen (PSA) concentration among PCa-free men. Longitudinal data were derived from electronic medical records at a tertiary hospital in the Southeastern USA. PCa-free men with initial-PSA < 4 ng/mL and ≥ 2 testosterone measurements were included (n = 632). Three PSA measures (peak, the most recent, and average PSA) during the study period (from first testosterone measurement to the most recent hospital visit) were examined using multivariable-adjusted geometric means and were compared across quintiles of testosterone change rate (ng/dL/month) and current testosterone level (cross-sectional). Mean (standard deviation, SD) age at baseline was 59.3 (10.5) years; mean study period was 93.0 (55.3) months. After adjusting for covariates including baseline testosterone, the three PSA measures all significantly increased across quintile of testosterone change rate from increase to decline (peak PSA: quint 1 = 1.09, quint 5 = 1.41; the most recent PSA: quint 1 = 0.85, quint 5 = 1.00; average PSA: quint 1 = 0.89, quint 5 = 1.02; all Ptrend < 0.001). But current testosterone level was not associated with PSA levels. Stratified analyses indicated men with higher adiposity (body mass index > 24.1 kg/m2) or lower baseline testosterone (≤ 296 ng/dL) were more sensitive to testosterone change in regard to PSA. Among PCa-free men, accelerated testosterone decline might correlate with higher serum PSA concentration. It will help to elucidate the mechanisms relating aging-accompanying testosterone dynamics to prostate carcinogenesis.

Understanding Perceptions and Attitudes in Breast Cancer Discussions on Twitter.

Among American women, the rate of breast cancer is only second to lung cancer. An estimated 12.4% women will develop breast cancer over the course of their lifetime. The widespread use of social media across the socio-economic spectrum offers unparalleled ways to facilitate information sharing, in particular as it pertains to health. Social media is also used by many healthcare stakeholders, ranging from government agencies to healthcare industry, to disseminate health information and to engage patients. The purpose of this study is to investigate people's perceptions and attitudes related to breast cancer, especially those that are related to physical activities, on Twitter. To achieve this, we first identified and collected tweets related to breast cancer; and then used topic modeling and sentiment analysis techniques to understand discussion themes and quantify Twitter users' perceptions and emotions with respect tobreast cancer to answer 5 research questions.

Exploring Lung Cancer Screening Discussions on Twitter.

Lung cancer is the leading cause of cancer-related death in the United States. Low-dose computed tomography (LDCT) for lung cancer screening (LCS) can reduce lung cancer deaths by 20% compared to chest x-rays. Nevertheless, the uptake of LDCT is low for high-risk smokers. In this study we used Twitter data to understand laypeople's emotions towards LCS, find topics on LCS-related tweets, and assess the impact of promotional information on laypeople's discussions.

BMI trajectories and risk of overall and grade-specific prostate cancer: An observational cohort study among men seen for prostatic conditions.

BACKGROUND: Dynamic longitudinal patterns in body mass index (BMI) have been suggested to better predict health outcomes than static measures. Effects of BMI trajectories on prostate cancer (PCa) risk have not been thoroughly explored. METHODS: Cohort data were derived from electronic medical records of patients who were admitted to a tertiary-care hospital in the Southeastern USA during 1994-2016. Patients with a history of urologic clinic visit because of any prostatic condition and with repeatedly measured BMI (n = 4857) were included. BMI trajectories prior to PCa diagnosis were assessed using the developmental trajectory analysis method. Cox proportional hazards regression modeling was used to estimate adjusted hazard ratio (aHR) with 95% confidence intervals (CIs) for overall and grade-specific PCa. RESULTS: The median age (interquartile range, IQR) of the participants at baseline was 63 (54, 72) years. Over a median follow-up (IQR) of 8.0 (2.0, 13.0) years, 714 (14.7%, 714/4857) were diagnosed with PCa. Men with growing BMI trajectory progressing from normal weight to overweight/obese had a 76% increased PCa risk (aHR = 1.76; 95% CI: 1.25, 2.48), and men being obese and experiencing progressive weight gain had 3.72-fold increased PCa risk (aHR = 3.72; 95% CI: 1.60, 8.66), compared to men with persistently normal BMI. The associations were more pronounced for PCa with Gleason score ≥7. No significant association of decreasing BMI trajectory progressing from obese to normal BMI was found with PCa risk. CONCLUSIONS: Progressively body weight gain during middle-to-late adulthood was associated with increased PCa risk for both normal weight and overweight men. Further studies are warranted to confirm this finding.

Multi-drug resistant Pseudomonas aeruginosa nosocomial strains: Molecular epidemiology and evolution.

Pseudomonas aeruginosa causes a wide variety of nosocomial infections. In the study, phylogenetic, selective pressure analysis and homology modelling were applied to oprD efflux pump gene with the aim to characterize multi-drug resistant strains circulating in the nosocomial setting, their transmission dynamics and ongoing evolution. One hundred ninety-three consecutive inpatients with Pseudomonas aeruginosa infection were enrolled at the University Campus Bio-Medico of Rome, between January 2015 and December 2016. oprD gene was sequenced in 20 nosocomial multi-drug resistant P. aeruginosa strains. Phylogeographic, selective pressure, residue conservation analysis and homology modelling were performed. Clinical epidemiological data were extracted from patient medical records. Multi-drug resistant strains accounted for the 36% of total strains and were responsible of 20 cases of nosocomial infections. P. aeruginosa infections occurred prevalently in the West area, especially at the location IIIW and in the Geriatric ward. The time of the most recent common ancestor indicated that strains could have been introduced in the hospital since the end of the year 2009 with the most probable location in general surgery ward. By selective pressure analysis, 29 positions under diversifying selection have been identified and mapped onto the OprD model. Most of the observed residue substitutions are predicted to be destabilizing and some of them occurred in the Loops 2 and 3 that are involved in solute selection and carbapenem susceptibility. The molecular and evolutionary analysis of Multi-drug resistant strains circulating in the nosocomial setting may provide useful insights into the epidemiology and the mechanisms leading to resistance, contributing to infection control improvement.

The role of benzathine penicillin G in predicting and preventing all-cause acute respiratory disease in military recruits: 1991-2017.

The adenovirus vaccine and benzathine penicillin G (BPG) have been used by the US military to prevent acute respiratory diseases (ARD) in trainees, though these interventions have had documented manufacturing problems. We fit Poisson regression and random forest models (RF) to 26 years of weekly ARD incidence data to explore the impact of the adenovirus vaccine and BPG prophylaxis on respiratory disease burden. Adenovirus vaccine availability was among the most important predictors of ARD in the RF, while BPG was the ninth most important. BPG was a significant protective factor against ARD (incidence rate ratio (IRR) = 0.68; 95% confidence interval (CI) 0.67-0.70), but less so than either the old or new adenovirus vaccine (IRR = 0.39, 95% CI 0.38-0.39 and IRR = 0.11, 95% CI 0.11-0.11), respectively. These results suggest that BPG is moderately predictive of, and significantly protective against ARD, though to a lesser extent than either the old or new adenovirus vaccine.