Last data update: Jun 03, 2024. (Total: 46935 publications since 2009)
Records 1-5 (of 5 Records) |
Query Trace: Gleason BL [original query] |
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Novel outbreak-associated food vehicles, United States
Whitham HK , Sundararaman P , Dewey-Mattia D , Manikonda K , Marshall KE , Griffin PM , Gleason BL , Subramhanya S , Crowe SJ . Emerg Infect Dis 2021 27 (10) 2554-2559 Novel outbreak-associated food vehicles (i.e., foods not implicated in past outbreaks) can emerge as a result of evolving pathogens and changing consumption trends. To identify these foods, we examined data from the Centers for Disease Control and Prevention Foodborne Disease Outbreak Surveillance System and found 14,216 reported outbreaks with information on implicated foods. We compared foods implicated in outbreaks during 2007-2016 with those implicated in outbreaks during 1973-2006. We identified 28 novel food vehicles, of which the most common types were fish, nuts, fruits, and vegetables; one third were imported. Compared with other outbreaks, those associated with novel food vehicles were more likely to involve illnesses in multiple states and food recalls and were larger in terms of cases, hospitalizations, and deaths. Two thirds of novel foods did not require cooking after purchase. Prevention efforts targeting novel foods cannot rely solely on consumer education but require industry preventive measures. |
Shiga toxin-producing Escherichia coli outbreaks in the United States, 20102017
Tack DM , Kisselburgh HM , Richardson LC , Geissler A , Griffin PM , Payne DC , Gleason BL . Microorganisms 2021 9 (7) Shiga toxin-producing Escherichia coli (STEC) cause illnesses ranging from mild diarrhea to ischemic colitis and hemolytic uremic syndrome (HUS); serogroup O157 is the most common cause. We describe the epidemiology and transmission routes for U.S. STEC outbreaks during 2010– 2017. Health departments reported 466 STEC outbreaks affecting 4769 persons; 459 outbreaks had a serogroup identified (330 O157, 124 non-O157, 5 both). Among these, 361 (77%) had a known transmission route: 200 foodborne (44% of O157 outbreaks, 41% of non-O157 outbreaks), 87 person-toperson (16%, 24%), 49 animal contact (11%, 9%), 20 water (4%, 5%), and 5 environmental contamination (2%, 0%). The most common food category implicated was vegetable row crops. The distribution of O157 and non-O157 outbreaks varied by age, sex, and severity. A significantly higher percentage of STEC O157 than non-O157 outbreaks were transmitted by beef (p = 0.02). STEC O157 outbreaks also had significantly higher rates of hospitalization and HUS (p < 0.001). © 2021 by the authors. Licensee MDPI, Basel, Switzerland. |
Implementing nationwide facility-based electronic disease surveillance in Sierra Leone: Lessons learned
Martin DW , Sloan ML , Gleason BL , de Wit L , Vandi MA , Kargbo DK , Clemens N , Kamara A , Njuguna C , Sesay S , Singh T . Health Secur 2020 18 S72-s80 The Global Health Security Agenda aims to improve countries' ability to prevent, detect, and respond to infectious disease threats by building or strengthening core capacities required by the International Health Regulations (2005). One of those capacities is the development of surveillance systems to rapidly detect and respond to occurrences of diseases with epidemic potential. Since 2015, the US Centers for Disease Control and Prevention (CDC) has worked with partners in Sierra Leone to assist the Ministry of Health and Sanitation in developing an Integrated Disease Surveillance and Response (IDSR) system. Beginning in 2016, CDC, in collaboration with the World Health Organization and eHealth Africa, has supported the ministry in the development of Android device mobile data entry at the health facility for electronic IDSR (eIDSR), also known as health facility-based eIDSR. Health facility-based eIDSR was introduced via a pilot program in 1 district, and national rollout began in 2018. With more than 1,100 health facilities now reporting, the Sierra Leone eIDSR system is substantially larger than most mobile-device health (mHealth) projects found in the literature. Several technical innovations contributed to the success of health facility-based eIDSR in Sierra Leone. Among them were data compression and dual-mode (internet and text) message transmission to mitigate connectivity issues, user interface design tailored to local needs, and a continuous-feedback process to iteratively detect user or system issues and remediate challenges identified. The resultant system achieved high user acceptance and demonstrated the feasibility of an mHealth-based surveillance system implemented on a national scale. |
Cost analysis of health facility electronic integrated disease surveillance and response in one district in Sierra Leone
Sloan ML , Gleason BL , Squire JS , Koroma FF , Sogbeh SA , Park MJ . Health Secur 2020 18 S64-s71 Global health security depends on effective surveillance systems to prevent, detect, and respond to disease threats. Real-time surveillance initiatives aim to develop electronic systems to improve reporting and analysis of disease data. Sierra Leone, with the support of Global Health Security Agenda partners, developed an electronic Integrated Disease Surveillance and Response (eIDSR) system capable of mobile reporting from health facilities. We estimated the economic costs associated with rollout of health facility eIDSR in the Western Area Rural district in Sierra Leone and projected annual direct operational costs. Cost scenarios with increased transport costs, decreased use of partner personnel, and altered cellular data costs were modeled. Cost data associated with activities were retrospectively collected and were assessed across rollout phases. Costs were organized into cost categories: personnel, office operating, transport, and capital. We estimated costs by category and phase and calculated per health facility and per capita costs. The total economic cost to roll out eIDSR to the Western Area Rural district over the 14-week period was US$64,342, a per health facility cost of $1,021. Equipment for eIDSR was the primary cost driver (45.5%), followed by personnel (35.2%). Direct rollout costs were $38,059, or 59.2% of total economic costs. The projected annual direct operational costs were $14,091, or $224 per health facility. Although eIDSR equipment costs are a large portion of total costs, annual direct operational costs are projected to be minimal once the system is implemented. Our findings can be used to make decisions about establishing and maintaining electronic, real-time surveillance in Sierra Leone and other low-resource settings. |
Geospatial analysis of household spread of Ebola virus in a quarantined village - Sierra Leone, 2014
Gleason BL , Foster S , Wilt GE , Miles B , Lewis B , Cauthen K , King M , Bayor F , Conteh S , Sesay T , Kamara SI , Lambert G , Finley P , Beyeler W , Moore T , Gaudioso J , Kilmarx PH , Redd JT . Epidemiol Infect 2017 145 (14) 1-9 We performed a spatial-temporal analysis to assess household risk factors for Ebola virus disease (Ebola) in a remote, severely-affected village. We defined a household as a family's shared living space and a case-household as a household with at least one resident who became a suspect, probable, or confirmed Ebola case from 1 August 2014 to 10 October 2014. We used Geographic Information System (GIS) software to calculate inter-household distances, performed space-time cluster analyses, and developed Generalized Estimating Equations (GEE). Village X consisted of 64 households; 42% of households became case-households over the observation period. Two significant space-time clusters occurred among households in the village; temporal effects outweighed spatial effects. GEE demonstrated that the odds of becoming a case-household increased by 4.0% for each additional person per household (P < 0.02) and 2.6% per day (P < 0.07). An increasing number of persons per household, and to a lesser extent, the passage of time after onset of the outbreak were risk factors for household Ebola acquisition, emphasizing the importance of prompt public health interventions that prioritize the most populated households. Using GIS with GEE can reveal complex spatial-temporal risk factors, which can inform prioritization of response activities in future outbreaks. |
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