Quarantine Stressing Voluntary Compliance

A 1-day table-top exercise in San Diego, California, in December 2004 emphasized voluntary compliance with home quarantine to control an emerging infectious disease outbreak. The exercise heightened local civilian-military collaboration in public health emergency management. Addressing concerns about lost income by residents in quarantine was particularly challenging

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and general additive modeling

Background A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Methods Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. Results The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. Conclusions The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data

Surveillance for Invasive Meningococcal Disease in Children, US–Mexico Border, 2005–20081

We reviewed confirmed cases of pediatric invasive meningococcal disease in Tijuana, Mexico, and San Diego County, California, USA, during 2005–2008. The overall incidence and fatality rate observed in Tijuana were similar to those found in the US, and serogroup distribution suggests that most cases in Tijuana are vaccine preventable

Modeling acute respiratory illness during the 2007 San Diego wildland fires using a coupled emissions-transport system and generalized additive modeling

A study of the impacts on respiratory health of the 2007 wildland fires in and around San Diego County, California is presented. This study helps to address the impact of fire emissions on human health by modeling the exposure potential of proximate populations to atmospheric particulate matter (PM) from vegetation fires. Currently, there is no standard methodology to model and forecast the potential respiratory health effects of PM plumes from wildland fires, and in part this is due to a lack of methodology for rigorously relating the two. The contribution in this research specifically targets that absence by modeling explicitly the emission, transmission, and distribution of PM following a wildland fire in both space and time. Coupled empirical and deterministic models describing particulate matter (PM) emissions and atmospheric dispersion were linked to spatially explicit syndromic surveillance health data records collected through the San Diego Aberration Detection and Incident Characterization (SDADIC) system using a Generalized Additive Modeling (GAM) statistical approach. Two levels of geographic aggregation were modeled, a county-wide regional level and division of the county into six sub regions. Selected health syndromes within SDADIC from 16 emergency departments within San Diego County relevant for respiratory health were identified for inclusion in the model. The model captured the variability in emergency department visits due to several factors by including nine ancillary variables in addition to wildfire PM concentration. The model coefficients and nonlinear function plots indicate that at peak fire PM concentrations the odds of a person seeking emergency care is increased by approximately 50% compared to non-fire conditions (40% for the regional case, 70% for a geographically specific case). The sub-regional analyses show that demographic variables also influence respiratory health outcomes from smoke. The model developed in this study allows a quantitative assessment and prediction of respiratory health outcomes as it relates to the location and timing of wildland fire emissions relevant for application to future wildfire scenarios. An important aspect of the resulting model is its generality thus allowing its ready use for geospatial assessments of respiratory health impacts under possible future wildfire conditions in the San Diego region. The coupled statistical and process-based modeling demonstrates an end-to-end methodology for generating reasonable estimates of wildland fire PM concentrations and health effects at resolutions compatible with syndromic surveillance data.https://doi.org/10.1186/1476-069X-12-9

Surveillance for Invasive Meningococcal Disease in Children, US–Mexico Border, 2005–20081

We reviewed confirmed cases of pediatric invasive meningococcal disease in Tijuana, Mexico, and San Diego County, California, USA, during 2005–2008. The overall incidence and fatality rate observed in Tijuana were similar to those found in the US, and serogroup distribution suggests that most cases in Tijuana are vaccine preventable

Phase 2b Controlled Trial of M72/AS01E Vaccine to Prevent Tuberculosis.

BACKGROUND: A vaccine to interrupt the transmission of tuberculosis is needed. METHODS: We conducted a randomized, double-blind, placebo-controlled, phase 2b trial of the M72/AS01E tuberculosis vaccine in Kenya, South Africa, and Zambia. Human immunodeficiency virus (HIV)-negative adults 18 to 50 years of age with latent M. tuberculosis infection (by interferon-γ release assay) were randomly assigned (in a 1:1 ratio) to receive two doses of either M72/AS01E or placebo intramuscularly 1 month apart. Most participants had previously received the bacille Calmette-Guérin vaccine. We assessed the safety of M72/AS01E and its efficacy against progression to bacteriologically confirmed active pulmonary tuberculosis disease. Clinical suspicion of tuberculosis was confirmed with sputum by means of a polymerase-chain-reaction test, mycobacterial culture, or both. RESULTS: We report the primary analysis (conducted after a mean of 2.3 years of follow-up) of the ongoing trial. A total of 1786 participants received M72/AS01E and 1787 received placebo, and 1623 and 1660 participants in the respective groups were included in the according-to-protocol efficacy cohort. A total of 10 participants in the M72/AS01E group met the primary case definition (bacteriologically confirmed active pulmonary tuberculosis, with confirmation before treatment), as compared with 22 participants in the placebo group (incidence, 0.3 cases vs. 0.6 cases per 100 person-years). The vaccine efficacy was 54.0% (90% confidence interval [CI], 13.9 to 75.4; 95% CI, 2.9 to 78.2; P=0.04). Results for the total vaccinated efficacy cohort were similar (vaccine efficacy, 57.0%; 90% CI, 19.9 to 76.9; 95% CI, 9.7 to 79.5; P=0.03). There were more unsolicited reports of adverse events in the M72/AS01E group (67.4%) than in the placebo group (45.4%) within 30 days after injection, with the difference attributed mainly to injection-site reactions and influenza-like symptoms. Serious adverse events, potential immune-mediated diseases, and deaths occurred with similar frequencies in the two groups. CONCLUSIONS: M72/AS01E provided 54.0% protection for M. tuberculosis-infected adults against active pulmonary tuberculosis disease, without evident safety concerns. (Funded by GlaxoSmithKline Biologicals and Aeras; ClinicalTrials.gov number, NCT01755598 .)

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype

Publisher Correction: Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

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