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Introduction On June 24, 2022, the United States Supreme Court overturned the landmark 1973 Roe v. Wade decision. This removed constitutional protection for women seeking abortions and introduced uncertainty for couples desiring reproductive autonomy. Currently, each state’s laws determine specific protections or bans for abortion. As a result, many individuals are investigating alternate forms of family planning such as vasectomy. Objective We aimed to characterize the public interest in vasectomy since the overturning of Roe v. Wade and elucidate whether interest varies based on state. Methods We extracted relative search volumes from Google Trends (July 2017 to July 2022) for the keyword “vasectomy”. We compared the relative rates of Google searches for the two weeks preceding and following the Supreme Court decision date of June 24, 2022. The mean rates were compared using a Welch t-test. Lastly, we assessed geographic variations in public interest after the decision by extracting the relative search data separated by state from Google Trends. Results Google searches peaked on June 25, 2022. This was the highest rate of Google searches for vasectomy within the past five years and was about four-fold higher than the average rate over that time (25.9%). There are two other prominent spikes: one on May 4, 2022 to about 60% of the highest rate, and another on May 16, 2019 to about 55% (Figure 1). In the two weeks following the Supreme Court announcement, there was a higher rate of searches for vasectomy (47.3%) compared to searches two weeks prior (17.2%) (P Conclusions The overturning of Roe v. Wade corresponds with the highest increase in Google searches for vasectomy in the past 5 years with a significant increase in searches the days following. In addition, the two previous spikes in vasectomy searches corresponded to events that threatened access to abortion. The spike in searches in May 2022 happened on the same day the draft of the Supreme Court’s decision on Roe v. Wade was leaked to the public. The spike in May 2019 correlates with the passing of a strict anti-abortion law in Alabama which received national attention. Interestingly, states with the highest rates of searches are also states with laws banning abortion in place. States with the lowest rates appear to be states with laws in place to protect access to abortion. Urologists should be aware of the unprecedented increase in public interest in vasectomy as it relates to a potential flux of patients seeking counseling for this procedure. Disclosure No

Background COVID-19 can cause serious illness requiring multimodal treatment of the viral infection and its associated complications, including the potential for secondary infections. Studies have suggested an increased risk of fungal infections, including candidemia following severe COVID-19 though understanding of risk factors and clinical outcomes remains unclear. Methods A multi-center, case-control study of patients with severe COVID-19 was conducted to evaluate risk factors and clinical outcomes in patients who developed candidemia between August 2020 to August 2021. Risk factors associated with candidemia and mortality were characterized using multivariate analyses. Results A total of 275 patients were enrolled in the study, including 91 patients with severe COVID-19 and subsequent candidemia and 184 patients with severe COVID-19 without candidemia. Most patients received antibiotics prior to candidemia episode (93%), while approximately one-quarter of all patients received biologic for COVID-19. In-hospital mortality was significantly higher in the case group compared to the control group (68% vs 40%, P < 0.01). Multivariable logistic regression revealed that the use of central lines, biologic and paralytic therapy were independent risk factors for candidemia. The presence of candidemia, older age, central line use, and intensive care unit admission were significantly associated with mortality. Demographics and Baseline Characteristics of Study Patients with SARS-CoV-2 Positive With or Without Candidemia Hospitalization Details and Outcomes Conclusion Clinicians should be aware of the possibility of development of candidemia in hospitalized older patients with severe COVID-19 and should closely monitor those patients at risk. Risk factors for developing candidemia in the setting of COVID-19 are largely consistent with classic risk factors previously identified. Disclosures Tanaya Bhowmick, MD, Gilead: Grant/Research Support|Shinogi: Advisor/Consultant Steven Smoke, PharmD, BCPS, BCIDP, Karius: Advisor/Consultant|Shionogi: Advisor/Consultant.

Background Modern transportation plays a key role in the spread of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and new variants. However, little is known about the exact transmission risk of the virus on airplanes. Methods Using the itinerary and epidemiological data of coronavirus disease 2019 (COVID-19) cases and close contacts on domestic airplanes departing from Wuhan city in China before the lockdown on 23 January 2020, we estimated the upper and lower bounds of overall transmission risk of COVID-19 among travelers. Results In total, 175 index cases were identified among 5797 passengers on 177 airplanes. The upper and lower attack rates (ARs) of a seat were 0.60% (34/5622, 95% confidence interval [CI] .43–.84%) and 0.33% (18/5400, 95% CI .21–.53%), respectively. In the upper- and lower-bound risk estimates, each index case infected 0.19 (SD 0.45) and 0.10 (SD 0.32) cases, respectively. The seats immediately adjacent to the index cases had an AR of 9.2% (95% CI 5.7–14.4%), with a relative risk 27.8 (95% CI 14.4–53.7) compared to other seats in the upper limit estimation. The middle seat had the highest AR (0.7%, 95% CI .4%–1.2%). The upper-bound AR increased from 0.7% (95% CI 0.5%–1.0%) to 1.2% (95% CI .4–3.3%) when the co-travel time increased from 2.0 hours to 3.3 hours. Conclusions The ARs among travelers varied by seat distance from the index case and joint travel time, but the variation was not significant between the types of aircraft. The overall risk of SARS-CoV-2 transmission during domestic travel on planes was relatively low. These findings can improve our understanding of COVID-19 spread during travel and inform response efforts in the pandemic.

2020 was an unprecedented year, with rapid and drastic changes in human mobility due to the COVID-19 pandemic. To understand the variation in commuting patterns among the Chinese population across stable and unstable periods, we used nationwide mobility data from 318 million mobile phone users in China to examine the extreme fluctuations of population movements in 2020, ranging from the Lunar New Year travel season (chunyun), to the exceptional calm of COVID-19 lockdown, and then to the recovery period. We observed that cross-city movements, which increased substantially in chunyun and then dropped sharply during the lockdown, are primarily dependent on travel distance and the socio-economic development of cities. Following the Lunar New Year holiday, national mobility remained low until mid-February, and COVID-19 interventions delayed more than 72.89 million people returning to large cities. Mobility network analysis revealed clusters of highly connected cities, conforming to the social-economic division of urban agglomerations in China. While the mass migration back to large cities was delayed, smaller cities connected more densely to form new clusters. During the recovery period after travel restrictions were lifted, the netflows of over 55% city pairs reversed in direction compared to before the lockdown. These findings offer the most comprehensive picture of Chinese mobility at fine resolution across various scenarios in China and are of critical importance for decision making regarding future public-health-emergency response, transportation planning and regional economic development, among others., This article gives an overview of commuting patterns in China in 2020, offering the most comprehensive understanding on national mobility at fine resolution across various scenarios in China.

Background The COVID-19 pandemic has posed an ongoing global crisis, but how the virus spread across the world remains poorly understood. This is of vital importance for informing current and future pandemic response strategies. Methods We performed two independent analyses, travel network-based epidemiological modelling and Bayesian phylogeographic inference, to investigate the intercontinental spread of COVID-19. Results Both approaches revealed two distinct phases of COVID-19 spread by the end of March 2020. In the first phase, COVID-19 largely circulated in China during mid-to-late January 2020 and was interrupted by containment measures in China. In the second and predominant phase extending from late February to mid-March, unrestricted movements between countries outside of China facilitated intercontinental spread, with Europe as a major source. Phylogenetic analyses also revealed that the dominant strains circulating in the USA were introduced from Europe. However, stringent restrictions on international travel across the world since late March have substantially reduced intercontinental transmission. Conclusions Our analyses highlight that heterogeneities in international travel have shaped the spatiotemporal characteristics of the pandemic. Unrestricted travel caused a large number of COVID-19 exportations from Europe to other continents between late February and mid-March, which facilitated the COVID-19 pandemic. Targeted restrictions on international travel from countries with widespread community transmission, together with improved capacity in testing, genetic sequencing and contact tracing, can inform timely strategies for mitigating and containing ongoing and future waves of COVID-19 pandemic.

Background Train travel is a common mode of public transport across the globe; however, the risk of coronavirus disease 2019 (COVID-19) transmission among individual train passengers remains unclear. Methods We quantified the transmission risk of COVID-19 on high-speed train passengers using data from 2334 index patients and 72 093 close contacts who had co-travel times of 0–8 hours from 19 December 2019 through 6 March 2020 in China. We analyzed the spatial and temporal distribution of COVID-19 transmission among train passengers to elucidate the associations between infection, spatial distance, and co-travel time. Results The attack rate in train passengers on seats within a distance of 3 rows and 5 columns of the index patient varied from 0 to 10.3% (95% confidence interval [CI], 5.3%–19.0%), with a mean of 0.32% (95% CI, .29%–.37%). Passengers in seats on the same row (including the adjacent passengers to the index patient) as the index patient had an average attack rate of 1.5% (95% CI, 1.3%–1.8%), higher than that in other rows (0.14% [95% CI, .11%–.17%]), with a relative risk (RR) of 11.2 (95% CI, 8.6–14.6). Travelers adjacent to the index patient had the highest attack rate (3.5% [95% CI, 2.9%–4.3%]) of COVID-19 infection (RR, 18.0 [95% CI, 13.9–23.4]) among all seats. The attack rate decreased with increasing distance, but increased with increasing co-travel time. The attack rate increased on average by 0.15% (P = .005) per hour of co-travel; for passengers in adjacent seats, this increase was 1.3% (P = .008), the highest among all seats considered. Conclusions COVID-19 has a high transmission risk among train passengers, but this risk shows significant differences with co-travel time and seat location. During disease outbreaks, when traveling on public transportation in confined spaces such as trains, measures should be taken to reduce the risk of transmission, including increasing seat distance, reducing passenger density, and use of personal hygiene protection.

Rationale for review The increasing mobility of populations allows pathogens to move rapidly and far, making endemic or epidemic regions more connected to the rest of the world than at any time in history. However, the ability to measure and monitor human mobility, health risk and their changing patterns across spatial and temporal scales using traditional data sources has been limited. To facilitate a better understanding of the use of emerging mobile phone technology and data in travel medicine, we reviewed relevant work aiming at measuring human mobility, disease connectivity and health risk in travellers using mobile geopositioning data. Key findings Despite some inherent biases of mobile phone data, analysing anonymized positions from mobile users could precisely quantify the dynamical processes associated with contemporary human movements and connectivity of infectious diseases at multiple temporal and spatial scales. Moreover, recent progress in mobile health (mHealth) technology and applications, integrating with mobile positioning data, shows great potential for innovation in travel medicine to monitor and assess real-time health risk for individuals during travel. Conclusions Mobile phones and mHealth have become a novel and tremendously powerful source of information on measuring human movements and origin–destination-specific risks of infectious and non-infectious health issues. The high penetration rate of mobile phones across the globe provides an unprecedented opportunity to quantify human mobility and accurately estimate the health risks in travellers. Continued efforts are needed to establish the most promising uses of these data and technologies for travel health.

Background Societal instability and crises can cause rapid, large-scale movements. These movements are poorly understood and difficult to measure but strongly impact health. Data on these movements are important for planning response efforts. We retrospectively analyzed movement patterns surrounding a 2010 humanitarian crisis caused by internal political conflict in Côte d'Ivoire using two different methods. Methods We used two remote measures, nighttime lights satellite imagery and anonymized mobile phone call detail records, to assess average population sizes as well as dynamic population changes. These data sources detect movements across different spatial and temporal scales. Results The two data sources showed strong agreement in average measures of population sizes. Because the spatiotemporal resolution of the data sources differed, we were able to obtain measurements on long- and short-term dynamic elements of populations at different points throughout the crisis. Conclusions Using complementary, remote data sources to measure movement shows promise for future use in humanitarian crises. We conclude with challenges of remotely measuring movement and provide suggestions for future research and methodological developments.

Globalization and the expansion of transport networks has transformed migration into a major policy issue because of its effects on a range of phenomena, including resource flows in economics, urbanization, as well as the epidemiology of infectious diseases. Quantifying and modeling human migration can contribute towards a better under- standing of the nature of migration and help develop evidence-based interventions for disease control policy, economic development, and resource allocation. In this study we paired census microdata from 10 countries in sub-Saharan Africa with additional spatial datasets to develop models for the internal migration flows in each country, including key drivers that reflect the changing social, demographic, economic, and environmental landscapes. We assessed how well these gravity-type spatial interaction models can both explain and predict migration. Results show that the models can explain up to 87 percent of internal migration, can predict future within-country mi- gration with correlations of up to 0.91, and can also predict migration in other coun- tries with correlations of up to 0.72. Findings show that such models are useful tools for understanding migration as well as predicting flows in regions where data are sparse, and can contribute towards strategic economic development, planning, and disease control targeting.

For most of human history, populations have been relatively isolated from each other, and only recently has there been extensive contact between peoples, flora and fauna from both old and new worlds. The reach, volume and speed of modern travel are unprecedented, with human mobility increasing in high income countries by over 1000-fold since 1800. This growth is putting people at risk from the emergence of new strains of familiar diseases, and from completely new diseases, while ever more cases of the movement of both disease vectors and the diseases they carry are being seen. Pathogens and their vectors can now move further, faster and in greater numbers than ever before. Equally however, we now have access to the most detailed and comprehensive datasets on human mobility and pathogen distributions ever assembled, in order to combat these threats. This short review paper provides an overview of these datasets, with a particular focus on low income regions, and covers briefly approaches used to combine them to help us understand and control some of the negative effects of population and pathogen movements.

Staphylococcus aureus is a common cause of infections that has undergone rapid global spread over recent decades. Formal phylogeographic methods have not yet been applied to the molecular epidemiology of bacterial pathogens because the limited genetic diversity of data sets based on individual genes usually results in poor phylogenetic resolution. Here, we investigated a whole-genome single nucleotide polymorphism (SNP) data set of health care-associated Methicillin-resistant S. aureus sequence type 239 (HA-MRSA ST239) strains, which we analyzed using Markov spatial models that incorporate geographical sampling distributions. The reconstructed timescale indicated a temporal origin of this strain shortly after the introduction of Methicillin, followed by global pandemic spread. The estimate of the temporal origin was robust to the molecular clock, coalescent prior, full/intergenic/synonymous SNP inclusion, and correction for excluded invariant site patterns. Finally, phylogeographic analyses statistically supported the role of human movement in the global dissemination of HA-MRSA ST239, although it was unable to conclusively resolve the location of the root. This study demonstrates that bacterial genomes can indeed contain sufficient evolutionary information to elucidate the temporal and spatial dynamics of transmission. Future applications of this approach to other bacterial strains may provide valuable epidemiological insights that may justify the cost of genome-wide typing.

We evaluated a combination respiratory syncytial virus (RSV) and parainfluenza 3 virus (PIV3) live, attenuated intranasal vaccine for safety, viral replication, and immunogenicity in doubly seronegative children 6-18 months old. RSV cpts-248/404 and PIV3-cp45 vaccines were combined in a dose of 10(5) plaque-forming units of each per 0.5-mL dose and compared with monovalent vaccines or placebo. The virus shedding pattern of RSV was not different between monovalent RSV cpts-248/404 vaccine and combination vaccine. Modest reductions in the shedding of PIV3-cp45 vaccine virus were found after the administration of RSV cpts-248/404 and PIV3-cp45 vaccine, relative to monovalent PIV3 vaccine; 16 (76%) of 21 children given combination vaccine shed PIV3-cp45 versus 11 (92%) of 12 of those given monovalent PIV3 vaccine. Both vaccines were immunogenic, and antibody responses were similar between the monovalent groups and the combination group. Combined RSV/PV3 vaccine is feasible for simultaneous administration, and further studies are warranted.

Summary: Studies of the human microbiome, and microbial community ecology in general, have blossomed of late and are now a burgeoning source of exciting research findings. Along with the advent of next-generation sequencing platforms, which have dramatically increased the scope of microbiome-related projects, several high-performance sequence analysis pipelines (e.g. QIIME, MOTHUR, VAMPS) are now available to investigators for microbiome analysis. The subject of our manuscript, the graphical user interface-based Explicet software package, fills a previously unmet need for a robust, yet intuitive means of integrating the outputs of the software pipelines with user-specified metadata and then visualizing the combined data. Availability and Implementation: Explicet is implemented in C++ via the Qt framework and supported in native code on all major operating systems (Windows, Macintosh, Linux). The source code, documents and tutorials are freely available under an open-source license at www.explicet.org. Contact: daniel.frank@ucdenver.edu