Optimizing the location of vaccination sites to stop a zoonotic epidemic.

Mass vaccinations are crucial public health interventions for curbing infectious diseases. Canine rabies control relies on mass dog vaccination campaigns (MDVCs) that are held annually across the globe. Dog owners must bring their pets to fixed vaccination sites, but sometimes target coverage is not achieved due to low participation. Travel distance to vaccination sites is an important barrier to participation. We aimed to increase MDVC participation in silico by optimally placing fixed-point vaccination locations. We quantified participation probability based on walking distance to the nearest vaccination site using regression models fit to participation data collected over 4 years. We used computational recursive interchange techniques to optimally place fixed-point vaccination sites and compared predicted participation with these optimally placed vaccination sites to actual locations used in previous campaigns. Algorithms that minimized average walking distance or maximized expected participation provided the best solutions. Optimal vaccination placement is expected to increase participation by 7% and improve spatial evenness of coverage, resulting in fewer under-vaccinated pockets. However, unevenness in workload across sites remained. Our data-driven algorithm optimally places limited resources to increase overall vaccination participation and equity. Field evaluations are essential to assess effectiveness and evaluate potentially longer waiting queues resulting from increased participation.

Enhancing Mass Vaccination Programs with Queueing Theory and Spatial Optimization.

Background: Mass vaccination is a cornerstone of public health emergency preparedness and response. However, injudicious placement of vaccination sites can lead to the formation of long waiting lines or queues , which discourages individuals from waiting to be vaccinated and may thus jeopardize the achievement of public health targets. Queueing theory offers a framework for modeling queue formation at vaccination sites and its effect on vaccine uptake. Methods: We developed an algorithm that integrates queueing theory within a spatial optimization framework to optimize the placement of mass vaccination sites. The algorithm was built and tested using data from a mass canine rabies vaccination campaign in Arequipa, Peru. We compared expected vaccination coverage and losses from queueing (i.e., attrition) for sites optimized with our queue-conscious algorithm to those obtained from a queue-naive version of the same algorithm. Results: Sites placed by the queue-conscious algorithm resulted in 9-19% less attrition and 1-2% higher vaccination coverage compared to sites placed by the queue-naïve algorithm. Compared to the queue-naïve algorithm, the queue-conscious algorithm favored placing more sites in densely populated areas to offset high arrival volumes, thereby reducing losses due to excessive queueing. These results were not sensitive to misspecification of queueing parameters or relaxation of the constant arrival rate assumption. Conclusion: One should consider losses from queueing to optimally place mass vaccination sites, even when empirically derived queueing parameters are not available. Due to the negative impacts of excessive wait times on participant satisfaction, reducing queueing attrition is also expected to yield downstream benefits and improve vaccination coverage in subsequent mass vaccination campaigns.

Mitigating Wildlife Spillover in the Clinical Setting: How Physicians and Veterinarians Can Help Prevent Future Disease Outbreaks.

Introduction: The transmission of pathogens from wildlife to humans is a major global health threat that has been highlighted by the proposed origins of the COVID-19 pandemic. Numerous barriers impede pathogen spillover events from ensuing widespread human transmission, but human activity has accelerated the frequency of spillovers and subsequent disease outbreaks, in part through a booming wildlife trade whose impacts on health are not well understood. Methods: A literature review was conducted to examine the risk that the wildlife trade poses to public health and the degree to which these risks are recognized and addressed in clinical practice and medical and veterinary education. Results: The illicit aspects of the wildlife trade challenge efforts to understand its impacts on health. The U.S. and Europe play a leading role in the global wildlife trade that often goes unacknowledged. In particular, the consumption of wild meat and ownership of exotic pets poses public health risks. The potential role of clinicians is underutilized, both in the clinical setting and in clinical education. Discussion: Physicians and veterinarians have the unique opportunity to utilize their clinical roles to address these knowledge gaps and mitigate future outbreaks. We outline a multifaceted approach that includes increasing clinical knowledge about the ecology of zoonotic diseases, leveraging opportunities for mitigation during patient/client-clinician interactions, and incorporating One Health core competencies into medical and veterinary school curricula.

Linking Ambient NO2 Pollution Measures with Electronic Health Record Data to Study Asthma Exacerbations.

Electronic health record (EHR)-derived data can be linked to geospatially distributed socioeconomic and environmental factors to conduct large-scale epidemiologic studies. Ambient NO2 is a known environmental risk factor for asthma. However, health exposure studies often rely on data from geographically sparse regulatory monitors that may not reflect true individual exposure. We contrasted use of interpolated NO2 regulatory monitor data with raw satellite measurements and satellite-derived ground estimates, building on previous work which has computed improved exposure estimates from remotely sensed data. Raw satellite and satellite-derived ground measurements captured spatial variation missed by interpolated ground monitor measurements. Multivariable analyses comparing these three NO2 measurement approaches (interpolated monitor, raw satellite, and satellite-derived) revealed a positive relationship between exposure and asthma exacerbations for both satellite measurements. Exposure-outcome relationships using the interpolated monitor NO2 were inconsistent with known relationships to asthma, suggesting that interpolated monitor data might yield misleading results in small region studies.

Neighborhood Characteristics and COVID-19 Incidence and Mortality in Southeastern Pennsylvania.

The COVID-19 pandemic has differentially impacted people according to their race/ethnicity, socioeconomic status, and preexisting conditions. Public health surveillance efforts, especially those occurring early in the pandemic, did not gather nor report adequate individual-level demographic information to identify these differences, and thus, neighborhood-level characteristics were used to note striking disparities in the US. We sought to determine whether risk factors associated with COVID-19 incidence and mortality in five Southeastern Pennsylvania counties could be better understood by using neighborhood-level demographic data augmented with health, socioeconomic, and environmental characteristics derived from publicly available sources. Although we found that education level and age of residents were the most salient predictors of COVID-19 incidence and mortality, respectively, neighborhoods exhibited a high degree of segregation with multiple correlated factors, which limits the ability of neighborhood-level analysis to identify actionable factors underlying COVID-19 disparities.

Feasibility and acceptability of monitoring personal air pollution exposure with sensors for asthma self-management.

BACKGROUND: Exposure to fine particulate matter (PM2.5) increases the risk of asthma exacerbations, and thus, monitoring personal exposure to PM2.5 may aid in disease self-management. Low-cost, portable air pollution sensors offer a convenient way to measure personal pollution exposure directly and may improve personalized monitoring compared with traditional methods that rely on stationary monitoring stations. We aimed to understand whether adults with asthma would be willing to use personal sensors to monitor their exposure to air pollution and to assess the feasibility of using sensors to measure real-time PM2.5 exposure. METHODS: We conducted semi-structured interviews with 15 adults with asthma to understand their willingness to use a personal pollution sensor and their privacy preferences with regard to sensor data. Student research assistants used HabitatMap AirBeam devices to take PM2.5 measurements at 1-s intervals while walking in Philadelphia neighborhoods in May-August 2018. AirBeam PM2.5 measurements were compared to concurrent measurements taken by three nearby regulatory monitors. RESULTS: All interview participants stated that they would use a personal air pollution sensor, though the consensus was that devices should be small (watch- or palm-sized) and light. Patients were generally unconcerned about privacy or sharing their GPS location, with only two stating they would not share their GPS location under any circumstances. PM2.5 measurements were taken using AirBeam sensors on 34 walks that extended through five Philadelphia neighborhoods. The range of sensor PM2.5 measurements was 0.6-97.6 µg/mL (mean 6.8 µg/mL), compared to 0-22.6 µg/mL (mean 9.0 µg/mL) measured by nearby regulatory monitors. Compared to stationary measurements, which were only available as 1-h integrated averages at discrete monitoring sites, sensor measurements permitted characterization of fine-scale fluctuations in PM2.5 levels over time and space. CONCLUSIONS: Patients were generally interested in using sensors to monitor their personal exposure to PM2.5 and willing to share personal sensor data with health care providers and researchers. Compared to traditional methods of personal exposure assessment, sensors captured personalized air quality information at higher spatiotemporal resolution. Improvements to currently available sensors, including more reliable Bluetooth connectivity, increased portability, and longer battery life would facilitate their use in a general patient population.

Consolidated Environmental and Social Data Facilitates Neighborhood-Level Health Studies in Philadelphia.

A wide range of datasets containing geographically distributed measures of the environment and social factors is currently available, and as low-cost sensors and other devices become increasingly used, the volume of these data will continue to grow. Because such factors influence many health outcomes, researchers with varied interests often repeat tasks related to gathering and preparing these data for studies. We created Sensor-based Analysis of Pollution in the Philadelphia Region with Information on Neighborhoods and the Environment (SAPPHIRINE), offered as a web application and R package, to integrate pollution, crime, social disadvantage, and traffic data relevant to investigators, citizen scientists, and policy makers in the Greater Philadelphia Area. SAPPHIRINE's capabilities include providing interactive maps and customizable data retrieval to aid in the visual identification of pollution and other factor hotspots, as well as hypothesis generation regarding relationships among these factors and health outcomes.

Neighborhood-level measures of socioeconomic status are more correlated with individual-level measures in urban areas compared with less urban areas.

PURPOSE: We tested the hypothesis that individual- and neighborhood-level measures of socioeconomic status (SES) are more concordant in urban than rural areas, and we used the previously established association between obesity and self-rated health to illustrate the effect of residual confounding by individual-level SES when only neighborhood-level SES is considered. METHODS: Using data from two population-based surveys, we calculated Spearman's rank correlations between household income and neighborhood socioeconomic advantage across eight Pennsylvania counties. We applied multivariable Poisson regression models with robust variance estimates to estimate the degree to which individual SES confounds the association between obesity and self-rated health when the analysis accounts for neighborhood SES only, and we examined how this confounding varied by county urbanicity. RESULTS: Concordance between household income and neighborhood advantage increased with county urbanicity (ρ = 0.16-0.26 vs. 0.31-0.45 vs. 0.47 in medium metro/micropolitan, suburban, and large metro counties, respectively), while confounding by individual SES on the obesity and self-rated health association decreased with urbanicity (15%-22% vs. 6%-13% vs. 3% in medium metro/micropolitan, suburban, and large metro counties, respectively). CONCLUSIONS: Individual- and neighborhood-level SES measures are poorly correlated outside of urban areas, suggesting that neighborhood-level measures inadequately account for individual SES in rural settings.

Impact of Individual versus Geographic-Area Measures of Socioeconomic Status on Health Associations Observed in the Behavioral Risk Factor Surveillance System.

Efforts to enhance Electronic Health Record (EHR) data for the study of conditions in which social and economic variables play a prominent role include linking clinical data to sources of external information via patient-specific geocodes. This approach is convenient, but whether geographic-area-level information from secondary sources is adequate as a surrogate of individual-level information is not fully understood. We used Behavioral Risk Factor Surveillance System (BRFSS) epidemiologic data to compare associations of individual income, median aggregate income, and Area Deprivation Index (ADI)-a validated score of U.S. socioeconomic deprivation-with various health outcomes. Median income and ADI assigned according to respondent area of residence were significantly associated with various health outcomes, but with substantially lower effect sizes than those of individual income. Our results show the limited ability of median income and ADI at the level of metropolitan/micropolitan statistical areas versus individual income for use as measures of socioeconomic status.

Dynamics of bed bug infestations and control under disclosure policies.

Bed bugs have reemerged in the United States and worldwide over recent decades, presenting a major challenge to both public health practitioners and housing authorities. A number of municipalities have proposed or initiated policies to stem the bed bug epidemic, but little guidance is available to evaluate them. One contentious policy is disclosure, whereby landlords are obligated to notify potential tenants of current or prior bed bug infestations. Aimed to protect tenants from leasing an infested rental unit, disclosure also creates a kind of quarantine, partially and temporarily removing infested units from the market. Here, we develop a mathematical model for the spread of bed bugs in a generalized rental market, calibrate it to parameters of bed bug dispersion and housing turnover, and use it to evaluate the costs and benefits of disclosure policies to landlords. We find disclosure to be an effective control policy to curb infestation prevalence. Over the short term (within 5 years), disclosure policies result in modest increases in cost to landlords, while over the long term, reductions of infestation prevalence lead, on average, to savings. These results are insensitive to different assumptions regarding the prevalence of infestation, rate of introduction of bed bugs from other municipalities, and the strength of the quarantine effect created by disclosure. Beyond its application to bed bugs, our model offers a framework to evaluate policies to curtail the spread of household pests and is appropriate for systems in which spillover effects result in highly nonlinear cost-benefit relationships.

Analysis of Spatial Trends in Smoking Status Among Patients with Obstructive Airway Diseases Highlight Potential for Targeted Smoking Cessation Interventions.

Because chronic obstructive airway diseases like asthma, chronic obstructive pulmonary disease (COPD) and asthma-COPD overlap (ACO) increase individual susceptibility to the harmful effects of cigarettes, smoking cessation programs could strengthen their public health impact by targeting smokers with these conditions. We performed spatial analyses on data derived from the Electronic Health Records (EHRs) of 25,119 asthma, 3,323 COPD, and 3,620 ACO patients and a community-based health survey of 18,740 residents to identify regions in the Greater Philadelphia Area with a high density of current smokers among patients with obstructive airway diseases and the general population. We identified areas in North and West Philadelphia with high prevalence of current smokers across all patient groups and community members that should be prioritized in smoking cessation initiatives. Neighborhood deprivation, which was linked to patient data using residential geocodes, was associated with greater smoking prevalence in these regions.

Approaches to Link Geospatially Varying Social, Economic, and Environmental Factors with Electronic Health Record Data to Better Understand Asthma Exacerbations.

Electronic health record (EHR)-derived data has become an invaluable resource for biomedical research, but is seldom used for the study of the health impacts of social and environmental factors due in part to the unavailability of relevant variables. We describe how EHR-derived data can be enhanced via linking of external sources of social, economic and environmental data when patient-related geospatial information is available, and we illustrate an approach to better understand the geospatial patterns of asthma exacerbation rates in Philadelphia. Specifically, we relate the spatial distribution of asthma exacerbations observed in EHR-derived data to that of known and potential risk factors (i.e., economic deprivation, crime, vehicular traffic, tree cover). Areas of highest risk based on integrated social and environmental data were consistent with an area with increased asthma exacerbations, demonstrating that data external to the EHR can enhance our understanding of negative health-related outcomes.

Enhancing Electronic Health Record Data with Geospatial Information.

Electronic Health Record (EHR)-derived data is a valuable resource for research, and efforts are underway to overcome some of its limitations by using data from external sources to gain a fuller picture of patient characteristics, symptoms, and exposures. Our goal was to assess the utility of augmenting EHR data with geocoded patient addresses to identify geospatial variation of disease that is not explained by EHR-derived demographic factors. Using 2011-2014 encounter data from 27,604 University of Pennsylvania Hospital System asthma patients, we identified factors associated with asthma exacerbations: risk was higher in female, black, middle aged to elderly, and obese patients, as well as those with positive smoking history and with Medicare or Medicaid vs. private insurance. Significant geospatial variability of asthma exacerbations was found using generalized additive models, even after adjusting for demographic factors. Our work shows that geospatial data can be used to cost-effectively enhance EHR data.

Heterogeneous infectiousness in guinea pigs experimentally infected with Trypanosoma cruzi.

Guinea pigs are important reservoirs of Trypanosoma cruzi, the causative parasite of Chagas disease, and in the Southern Cone of South America, transmission is mediated mainly by the vector Triatoma infestans. Interestingly, colonies of Triatoma infestans captured from guinea pig corrals sporadically have infection prevalence rates above 80%. Such high values are not consistent with the relatively short 7-8 week parasitemic period that has been reported for guinea pigs in the literature. We experimentally measured the infectious periods of a group of T. cruzi-infected guinea pigs by performing xenodiagnosis and direct microscopy each week for one year. Another group of infected guinea pigs received only direct microscopy to control for the effect that inoculation by triatomine saliva may have on parasitemia in the host. We observed infectious periods longer than those previously reported in a number of guinea pigs from both the xenodiagnosis and control groups. While some guinea pigs were infectious for a short time, other "super-shedders" were parasitemic up to 22 weeks after infection, and/or positive by xenodiagnosis for a year after infection. This heterogeneity in infectiousness has strong implications for T. cruzi transmission dynamics and control, as super-shedder guinea pigs may play a disproportionate role in pathogen spread.

Protein cysteine phosphorylation of SarA/MgrA family transcriptional regulators mediates bacterial virulence and antibiotic resistance.

Protein posttranslational modifications (PTMs), particularly phosphorylation, dramatically expand the complexity of cellular regulatory networks. Although cysteine (Cys) in various proteins can be subject to multiple PTMs, its phosphorylation was previously considered a rare PTM with almost no regulatory role assigned. We report here that phosphorylation occurs to a reactive cysteine residue conserved in the staphylococcal accessary regulator A (SarA)/MarR family global transcriptional regulator A (MgrA) family of proteins, and is mediated by the eukaryotic-like kinase-phosphatase pair Stk1-Stp1 in Staphylococcus aureus. Cys-phosphorylation is crucial in regulating virulence determinant production and bacterial resistance to vancomycin. Cell wall-targeting antibiotics, such as vancomycin and ceftriaxone, inhibit the kinase activity of Stk1 and lead to decreased Cys-phosphorylation of SarA and MgrA. An in vivo mouse model of infection established that the absence of stp1, which results in elevated protein Cys-phosphorylation, significantly reduces staphylococcal virulence. Our data indicate that Cys-phosphorylation is a unique PTM that can play crucial roles in bacterial signaling and regulation.

Quorum-sensing agr mediates bacterial oxidation response via an intramolecular disulfide redox switch in the response regulator AgrA.

Oxidation sensing and quorum sensing significantly affect bacterial physiology and host-pathogen interactions. However, little attention has been paid to the cross-talk between these two seemingly orthogonal signaling pathways. Here we show that the quorum-sensing agr system has a built-in oxidation-sensing mechanism through an intramolecular disulfide switch possessed by the DNA-binding domain of the response regulator AgrA. Biochemical and mass spectrometric analysis revealed that oxidation induces the intracellular disulfide bond formation between Cys-199 and Cys-228, thus leading to dissociation of AgrA from DNA. Molecular dynamics (MD) simulations suggest that the disulfide bond formation generates a steric clash responsible for the abolished DNA binding of the oxidized AgrA. Mutagenesis studies further established that Cys-199 is crucial for oxidation sensing. The oxidation-sensing role of Cys-199 is further supported by the observation that the mutant Staphylococcus aureus strain expressing AgrAC199S is more susceptible to H(2)O(2) owing to repression of the antioxidant bsaA gene under oxidative stress. Together, our results show that oxidation sensing is a component of the quorum-sensing agr signaling system, which serves as an intrinsic checkpoint to ameliorate the oxidation burden caused by intense metabolic activity and potential host immune response.