Last data update: Sep 30, 2024. (Total: 47785 publications since 2009)
Records 1-12 (of 12 Records) |
Query Trace: O'Hagan JJ [original query] |
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Improving mathematical modeling of interventions to prevent healthcare-associated infections by interrupting transmission or pathogens: How common modeling assumptions about colonized individuals impact intervention effectiveness estimates
Gowler CD , Slayton RB , Reddy SC , O'Hagan JJ . PLoS One 2022 17 (2) e0264344 Mathematical models are used to gauge the impact of interventions for healthcare-associated infections. As with any analytic method, such models require many assumptions. Two common assumptions are that asymptomatically colonized individuals are more likely to be hospitalized and that they spend longer in the hospital per admission because of their colonization status. These assumptions have no biological basis and could impact the estimated effects of interventions in unintended ways. Therefore, we developed a model of methicillin-resistant Staphylococcus aureus transmission to explicitly evaluate the impact of these assumptions. We found that assuming that asymptomatically colonized individuals were more likely to be admitted to the hospital or spend longer in the hospital than uncolonized individuals biased results compared to a more realistic model that did not make either assumption. Results were heavily biased when estimating the impact of an intervention that directly reduced transmission in a hospital. In contrast, results were moderately biased when estimating the impact of an intervention that decolonized hospital patients. Our findings can inform choices modelers face when constructing models of healthcare-associated infection interventions and thereby improve their validity. |
Modeling the potential impact of administering vaccines against Clostridioides difficile infection to individuals in healthcare facilities.
Toth DJA , Keegan LT , Samore MH , Khader K , O'Hagan JJ , Yu H , Quintana A , Swerdlow DL . Vaccine 2020 38 (37) 5927-5932 BACKGROUND: A vaccine against Clostridioides difficile infection (CDI) is in development. While the vaccine has potential to both directly protect those vaccinated and mitigate transmission by reducing environmental contamination, the impact of the vaccine on C. difficile colonization remains unclear. Consequently, the transmission-reduction effect of the vaccine depends on the contribution of symptomatic CDI to overall transmission of C. difficile. METHODS: We designed a simulation model of CDI among patients in a network of 10 hospitals and nursing homes and calibrated the model using estimates of transmissibility from whole genome sequencing studies that estimated the fraction of CDI attributable to transmission from other CDI patients. We assumed the vaccine reduced the rate of progression to CDI among carriers by 25-95% after completion of a 3-dose vaccine course administered to randomly chosen patients at facility discharge. We simulated the administration of this vaccination campaign and tallied effects over 5 years. RESULTS: We estimated 30 times higher infectivity of CDI patients compared to other carriers. Simulations of the vaccination campaign produced an average reduction of 3-16 CDI cases per 1000 vaccinated patients, with 2-11 of those cases prevented among those vaccinated and 1-5 prevented among unvaccinated patients. CONCLUSIONS: Our findings demonstrate potential for a vaccine against CDI to reduce transmissions in healthcare facilities, even with no direct effect on carriage susceptibility. The vaccine's population impact will increase if received by individuals at risk for CDI onset in high-transmission settings. |
Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) framework for describing and reporting multidrug resistant organism and healthcare-associated infections agent-based modeling methods
Slayton RB , O'Hagan JJ , Barnes S , Rhea S , Hilscher R , Rubin M , Lofgren E , Singh B , Segre A , Paul P . Clin Infect Dis 2020 71 (9) 2527-2532 Mathematical modeling of healthcare associated infections (HAIs) and multidrug resistant organisms (MDROs) improves our understanding of pathogens transmission dynamics and provides a framework for evaluating prevention strategies. One way of improving the communication among modelers is by providing a standardized way of describing and reporting models thereby instilling confidence in the reproducibility and generalizability of such models. We updated the Overview, Design concepts, and Details protocol developed by Grimm et al. for describing agent-based models (ABMs) to better align with elements commonly included in healthcare-related ABMs. The MInD-Healthcare framework includes the following nine key elements: 1. Purpose and scope; 2. Entities, state variables, and scales; 3. Initialization; 4. Process overview and scheduling; 5. Input data; 6. Agent interactions and organism transmission; 7. Stochasticity; 8. Submodels; 9. Model verification, calibration, and validation. Our objective is that this framework will improve the quality of evidence generated utilizing these models. |
Forecasting the 2014 West African Ebola outbreak
Carias C , O'Hagan JJ , Gambhir M , Kahn EB , Swerdlow DL , Meltzer MI . Epidemiol Rev 2019 41 (1) 34-50 In 2014/15 an Ebola outbreak of unprecedented dimensions afflicted the West African countries of Liberia, Guinea, and Sierra Leone. We performed a systematic review of manuscripts that forecasted the outbreak while it was occurring, and derive implications on the ways results could be interpreted by policy-makers. We reviewed 26 manuscripts, published between 2014 and April 2015, that presented forecasts of the West African Ebola outbreak. Forecasted case counts varied widely. An important determinant of forecast accuracy for case counts was how far into the future predictions were made. Generally, those that made forecasts less than 2 months into the future tended to be more accurate than those that made forecasts more than 10 weeks into the future. The exceptions were parsimonious statistical models in which the decay of the rate of spread of the pathogen among susceptible individuals was dealt with explicitly. Regarding future outbreaks, the most important lessons for policy makers when using similar modeling results are: i) uncertainty of forecasts will be higher in the beginning of the outbreak, ii) when data are limited, forecasts produced by models designed to inform specific decisions should be used in complimentary fashion for robust decision making - for this outbreak, two statistical models produced the most reliable case counts forecasts, but did not allow to understand the impact of interventions, while several compartmental models could estimate the impact of interventions but required data that was not available; iii) timely collection of essential data is necessary for optimal model use. |
The challenges of tracking Clostridium difficile to its source in hospitalized patients
O'Hagan JJ , McDonald LC . Clin Infect Dis 2018 68 (2) 210-212 Clostridium difficile is responsible for between 400,000 and 500,000 infections in the United States each year and is the leading cause of healthcare-associated infections [1, 2]. A major risk factor for Clostridium difficile infection (CDI) is a current or recent stay in a hospital, where rates of both symptomatic CDI and asymptomatic colonization are higher than in the community [3–6]. Infection control recommendations for hospitals focus on preventing transmission from symptomatic CDI patients; active surveillance testing to detect asymptomatically-colonized patients is not currently recommended [7]. However, asymptomatically-colonized patients, as well as patients with active CDI, may cause CDI through transmission to other patients, although their relative contributions to overall transmission remain uncertain [8–11]. For example, using restriction enzyme analysis typing, Clabots et al found that 84% (16/19) of hospital acquisitions were linked to asymptomatic carriers while, in another study that used variable-number tandem repeats genotyping, 29% (16/56) of acquisitions were linked to carriers [9, 10]. Transmission occurs via C. difficile spores that are resistant to commonly-used hand sanitizers and environmental disinfectants [3]. Therefore, evidence that asymptomatic carriers are responsible for a large amount of transmission within hospitals would prompt new interventions designed to reduce transmission from such patients. In this issue, Kong et al report findings from the largest study to date investigating the relative roles of carriers and cases as sources of transmission to patients with CDI [12]. |
The potential for interventions in a long-term acute care hospital to reduce transmission of carbapenem-resistant Enterobacteriaceae in affiliated healthcare facilities
Toth DJA , Khader K , Slayton RB , Kallen AJ , Gundlapalli AV , O'Hagan JJ , Fiore AE , Rubin MA , Jernigan JA , Samore MH . Clin Infect Dis 2017 65 (4) 581-587 Background.: Carbapenem-resistant Enterobacteriaceae (CRE) are high-priority bacterial pathogens targeted for efforts to decrease transmissions and infections in healthcare facilities. Some regions have experienced CRE outbreaks that were likely amplified by frequent transmission in long-term acute care hospitals (LTACHs). Planning and funding intervention efforts focused on LTACHs is one proposed strategy to contain outbreaks; however, the potential regional benefits of such efforts are unclear. Methods.: We designed an agent-based simulation model of patients in a regional network of 10 healthcare facilities including 1 LTACH, 3 short-stay acute care hospitals (ACHs) and 6 nursing homes (NHs). The model was calibrated to achieve realistic patient flow and CRE transmission and detection rates. We then simulated the initiation of an entirely LTACH-focused intervention in a previously CRE-free region, including active surveillance for CRE carriers and enhanced isolation of identified carriers. Results.: When initiating the intervention at the 1st clinical CRE detection in the LTACH, cumulative CRE transmissions over 5 years across all 10 facilities were reduced by 79-93% compared to no-intervention simulations. This result was robust to changing assumptions for transmission within non-LTACH facilities and flow of patients from the LTACH. Delaying the intervention until the 20th CRE detection resulted in substantial delays in achieving optimal regional prevalence, while still reducing transmissions by 60-79% over 5 years. Conclusions.: Focusing intervention efforts on LTACHs is potentially a highly efficient strategy for reducing CRE transmissions across an entire region, particularly when implemented as early as possible in an emerging outbreak. |
Temporally varying relative risks for infectious diseases: implications for infectious disease control
Goldstein E , Pitzer VE , O'Hagan JJ , Lipsitch M . Epidemiology 2016 28 (1) 136-144 Risks for disease in some population groups relative to others (relative risks) are usually considered to be consistent over time, though they are often modified by other, non-temporal factors. For infectious diseases, in which overall incidence often varies substantially over time, the patterns of temporal changes in relative risks can inform our understanding of basic epidemiologic questions. For example, recent work suggests that temporal changes in relative risks of infection over the course of an epidemic cycle can both be used to identify population groups that drive infectious disease outbreaks, and help elucidate differences in the effect of vaccination against infection (that is relevant to transmission control) compared with its effect against disease episodes (that reflects individual protection). Patterns of change in the in age groups affected over the course of seasonal outbreaks can provide clues to the types of pathogens that could be responsible for diseases for which an infectious cause is suspected. Changing apparent efficacy of vaccines during trials may provide clues to the vaccine's mode of action and/or indicate risk heterogeneity in the trial population. Declining importance of unusual behavioral risk factors may be a signal of increased local transmission of an infection. We review these developments and the related public health implications. |
Estimation of severe Middle East Respiratory Syndrome cases in the Middle East, 2012-2016
O'Hagan JJ , Carias C , Rudd JM , Pham HT , Haber Y , Pesik N , Cetron MS , Gambhir M , Gerber SI , Swerdlow DL . Emerg Infect Dis 2016 22 (10) 1797-9 Using data from travelers to 4 countries in the Middle East, we estimated 3,250 (95% CI 1,300-6,600) severe cases of Middle East respiratory syndrome occurred in this region during September 2012-January 2016. This number is 2.3-fold higher than the number of laboratory-confirmed cases recorded in these countries. |
Exportations of symptomatic cases of MERS-CoV infection to countries outside the Middle East
Carias C , O'Hagan JJ , Jewett A , Gambhir M , Cohen NJ , Haber Y , Pesik N , Swerdlow DL . Emerg Infect Dis 2016 22 (3) 723-5 In 2012, an outbreak of infection with Middle East respiratory syndrome coronavirus (MERS-CoV), was detected in the Arabian Peninsula. Modeling can produce estimates of the expected annual number of symptomatic cases of MERS-CoV infection exported and the likelihood of exportation from source countries in the Middle East to countries outside the region. |
Estimating the United States demand for influenza antivirals and the effect on severe influenza disease during a potential pandemic
O'Hagan JJ , Wong KK , Campbell AP , Patel A , Swerdlow DL , Fry AM , Koonin LM , Meltzer MI . Clin Infect Dis 2015 60 Suppl 1 S30-41 Following the detection of a novel influenza strain A(H7N9), we modeled the use of antiviral treatment in the United States to mitigate severe disease across a range of hypothetical pandemic scenarios. Our outcomes were total demand for antiviral (neuraminidase inhibitor) treatment and the number of hospitalizations and deaths averted. The model included estimates of attack rate, healthcare-seeking behavior, prescription rates, adherence, disease severity, and the potential effect of antivirals on the risks of hospitalization and death. Based on these inputs, the total antiviral regimens estimated to be available in the United States (as of April 2013) were sufficient to meet treatment needs for the scenarios considered. However, distribution logistics were not examined and should be addressed in future work. Treatment was estimated to avert many severe outcomes (5200-248 000 deaths; 4800-504 000 hospitalizations); however, large numbers remained (25 000-425 000 deaths; 580 000-3 700 000 hospitalizations), suggesting that the impact of combinations of interventions should be examined. |
Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential
Gambhir M , Bozio C , O'Hagan JJ , Uzicanin A , Johnson LE , Biggerstaff M , Swerdlow DL . Clin Infect Dis 2015 60 Suppl 1 S11-9 The rising importance of infectious disease modeling makes this an appropriate time for a guide for public health practitioners tasked with preparing for, and responding to, an influenza pandemic. We list several questions that public health practitioners commonly ask about pandemic influenza and match these with analytical methods, giving details on when during a pandemic the methods can be used, how long it might take to implement them, and what data are required. Although software to perform these tasks is available, care needs to be taken to understand: (1) the type of data needed, (2) the implementation of the methods, and (3) the interpretation of results in terms of model uncertainty and sensitivity. Public health leaders can use this article to evaluate the modeling literature, determine which methods can provide appropriate evidence for decision-making, and to help them request modeling work from in-house teams or academic groups. |
Mobile messaging as surveillance tool during pandemic (H1N1) 2009, Mexico
Lajous M , Danon L , Lopez-Ridaura R , Astley CM , Miller JC , Dowell SF , O'Hagan JJ , Goldstein E , Lipsitch M . Emerg Infect Dis 2010 16 (9) 1488-9 To the Editor: Pandemic (H1N1) 2009 highlighted challenges faced by disease surveillance systems. New approaches to complement traditional surveillance are needed, and new technologies provide new opportunities. We evaluated cell phone technology for surveillance of influenza outbreaks during the outbreak of pandemic (H1N1) 2009 in Mexico. |
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