Measuring dengue illness intensity: Development and content validity of the dengue virus daily diary (DENV-DD).

BACKGROUND: Dengue is the most prevalent arboviral infection causing an estimated 50-60 million cases of febrile illness globally per year, exacting considerable disease burden. Few instruments exist to assess the patient illness experience, with most based on healthcare provider assessment, lacking standardization in timepoints and symptom assessment. This study aimed to evaluate the content validity of the novel 'Dengue Virus Daily Diary (DENV-DD)', designed to measure symptom intensity and disease burden within outpatient infant to adult populations. METHODS: The Dengue Illness Index Report Card was used as a foundation to create the DENV-DD, consisting of patient- and observer-reported outcome (PRO/ObsRO) instruments. In two South American dengue-endemic communities, qualitative combined concept elicitation and cognitive debriefing interviews were conducted among individuals and caregivers of children with symptomatic laboratory-confirmed dengue. Interviews were conducted across two rounds allowing DENV-DD modifications. A small-scale quantitative assessment of the DENV-DD was also conducted with data from an independent Dengue Human Infection Model (DHIM) to generate early evidence of feasibility of DENV-DD completion, instrument performance and insight into the sign/symptom trajectory over the course of illness. RESULTS: Forty-eight participants were interviewed (20 adults, 20 older children/adolescents with their caregivers, 8 caregivers of younger children). A wide spectrum of signs/symptoms lasting 3-15 days were reported with fever, headache, body ache/pain, loss of appetite, and body weakness each reported by > 70% participants. DENV-DD instructions, items and response scales were understood, and items were considered relevant across ages. DHIM data supported feasibility of DENV-DD completion. CONCLUSIONS: Findings demonstrate content validity of the DENV-DD (PRO/ObsRO instruments) in dengue-endemic populations. Psychometric and cultural validity studies are ongoing to support use of the DENV-DD in clinical studies.

Dengue is the most common viral infection transmitted to humans by mosquitos, and affects an estimated 50­60 million individuals globally per year. However, there are few resources for understanding and capturing the patient experience of dengue throughout illness. Most research studies are based on healthcare provider assessment, which lack consistency in terms of assessment time points and the signs/symptoms assessed. The 'Dengue Illness Index Report Card (DII-RC)' was used as a foundation to create the new 'Dengue Virus Daily Diary (DENV-DD)' to better capture the patient experience of symptom intensity and dengue disease burden for the duration of illness. Forty-eight individuals and caregivers of younger children from Peru and Ecuador who recently had symptomatic dengue were interviewed to understand the patient experience over the time of illness and to test whether the DENV-DD is understood by patients and caregivers and includes all relevant and important signs/symptoms and health-related quality of life impacts. Nine individuals with active dengue infection also completed the DENV-DD daily for 28-days as part of a clinical study. We found that > 70% of patients experienced fever, headache, body ache/pain, loss of appetite and body weakness. The DENV-DD instructions, questions and response option(s) were well understood, feasible to complete and the concepts assessed by the DENV-DD were relevant to the dengue experience. Our study adds to the understanding of the dengue illness experience and supports the DENV-DD for use in future dengue studies as an assessment of signs/symptoms throughout the duration of illness.

Natural History of Clostridioides difficile Colonization and Infection Following New Acquisition of Carriage in Healthcare Settings: A Prospective Cohort Study.

BACKGROUND: Limited information is available on the natural history of Clostridioides difficile colonization and infection in patients with new acquisition of C. difficile in healthcare settings. METHODS: In 3 hospitals and affiliated long-term care facilities, we collected serial perirectal cultures from patients with no diarrhea on enrollment to identify new acquisition of toxigenic C. difficile carriage and determined the duration and burden of carriage. Asymptomatic carriage was defined as transient if only 1 culture was positive, with negative cultures before and after, or persistent if 2 or more cultures were positive. Clearance of carriage was defined as 2 consecutive negative perirectal cultures. RESULTS: Of 1432 patients with negative initial cultures and at least 1 follow-up culture, 39 (2.7%) developed C. difficile infection (CDI) without prior detection of carriage and 142 (9.9%) acquired asymptomatic carriage, with 19 (13.4%) subsequently diagnosed with CDI. Of 82 patients analyzed for persistence of carriage, 50 (61.0%) had transient carriage and 32 (39.0%) had persistent carriage, with an estimated median of 77 days to clearance of colonization (range, 14-133 days). Most persistent carriers had a relatively high burden of carriage and maintained the same ribotype over time, whereas most transient carriers had a low burden of carriage detected only using broth enrichment cultures. CONCLUSIONS: In 3 healthcare facilities, 9.9% of patients acquired asymptomatic carriage of toxigenic C. difficile, and 13.4% were subsequently diagnosed with CDI. Most carriers had transient rather than persistent carriage and most patients developing CDI did not have prior detection of carriage.

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.

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.

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.

Photothermal therapy.

Microneedles (MNs), as an effective minimally invasive delivery route, when used to puncture the skin, can bypass the skin's stratum corneum (SC) to enter the skin microcirculation and achieve systemic administration. Additionally, the MN route has obvious advantages over other routes of administration, including simplicity, non-pain, readily-permitted transport of drugs (including DNA and metformin) and macromolecules (such as antibodies and proteins), good repeatability, and wide range of clinical applications and safety. MNs have been combined with various therapy strategies including photodynamic therapy (PDT) and photothermal therapy (PTT) to treat many diseases, and hold great promise for improving the diagnosis and treatment of diseases. Both MN-assisted PDT and PTT are light-mediated phototherapy methods and have unique advantages, including improved selectivity, and minimal invasiveness and side effects. MN-assisted PDT or PTT has been studied for various applications by many research groups and pharmaceutical companies worldwide. Therefore, this review summarizes recent advances in MNs for PDT or PTT.

Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) Framework for Describing and Reporting Multidrug-resistant Organism and Healthcare-Associated Infections Agent-based Modeling Methods.

Mathematical modeling of healthcare-associated infections and multidrug-resistant organisms improves our understanding of pathogen 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 [11] for describing agent-based models (ABMs) to better align with elements commonly included in healthcare-related ABMs. The Modeling Infectious Diseases in Healthcare Network (MInD-Healthcare) framework includes the following 9 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; and (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.

In 2014-2015, a large Ebola outbreak afflicted Liberia, Guinea, and Sierra Leone. We performed a systematic review of 26 manuscripts, published between 2014 and April 2015, that forecasted the West African Ebola outbreak while it was occurring, and we derived implications for how results could be interpreted by policymakers. Forecasted case counts varied widely. An important determinant of forecast accuracy for case counts was how far into the future predictions were made. Generally, forecasts for less than 2 months into the future tended to be more accurate than those made for 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. The most important lessons for policymakers regarding future outbreaks, when using similar modeling results, are: 1) uncertainty of forecasts will be greater in the beginning of the outbreak; 2) when data are limited, forecasts produced by models designed to inform specific decisions should be used complementarily for robust decision-making (e.g., 2 statistical models produced the most reliable case-counts forecasts for the studied Ebola outbreak but did not enable understanding of interventions' impact, whereas several compartmental models could estimate interventions' impact but required unavailable data); and 3) timely collection of essential data is necessary for optimal model use.

Sample Size Estimates for Cluster-Randomized Trials in Hospital Infection Control and Antimicrobial Stewardship.

Importance: An important step in designing, executing, and evaluating cluster-randomized trials (CRTs) is understanding the correlation and thus nonindependence that exists among individuals in a cluster. In hospital epidemiology, there is a shortage of CRTs that have published their intraclass correlation coefficient or coefficient of variation (CV), making prospective sample size calculations difficult for investigators. Objectives: To estimate the number of hospitals needed to power parallel CRTs of interventions to reduce health care-associated infection outcomes and to demonstrate how different parameters such as CV and expected effect size are associated with the sample size estimates in practice. Design, Setting, and Participants: This longitudinal cohort study estimated parameters for sample size calculations using national rates developed by the Centers for Disease Control and Prevention for methicillin-resistant Staphylococcus aureus (MRSA) bacteremia, central-line-associated bloodstream infections (CLABSI), catheter-associated urinary tract infections (CAUTI), and Clostridium difficile infections (CDI) from 2016. For MRSA and vancomycin-resistant enterococci (VRE) acquisition, outcomes were estimated using data from 2012 from the Benefits of Universal Glove and Gown study. Data were collected from June 2017 through September 2018 and analyzed from September 2018 through January 2019. Main Outcomes and Measures: Calculated number of clusters needed for adequate power to detect an intervention effect using a 2-group parallel CRT. Results: To study an intervention with a 30% decrease in daily rates, 73 total clusters were needed (37 in the intervention group and 36 in the control group) for MRSA bacteremia, 82 for CAUTI, 60 for CLABSI, and 31 for CDI. If a 10% decrease in rates was expected, 768 clusters were needed for MRSA bacteremia, 875 for CAUTI, 631 for CLABSI, and 329 for CDI. For MRSA or VRE acquisition, 50 or 40 total clusters, respectively, were required to observe a 30% decrease, whereas 540 or 426 clusters, respectively, were required to detect a 10% decrease. Conclusions and Relevance: This study suggests that large sample sizes are needed to appropriately power parallel CRTs targeting infection prevention outcomes. Sample sizes are most associated with expected effect size and CV of hospital rates.

The Potential for Interventions in a Long-term Acute Care Hospital to Reduce Transmission of Carbapenem-Resistant Enterobacteriaceae in Affiliated Healthcare Facilities.

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 of 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 first 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.

Risks for disease in some population groups relative to others (relative risks) are usually considered to be consistent over time, although they are often modified by other, nontemporal 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 studies suggest 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 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.

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.

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.

Infectious disease modeling methods as tools for informing response to novel influenza viruses of unknown pandemic potential.

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.

Estimating the United States demand for influenza antivirals and the effect on severe influenza disease during a potential pandemic.

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.

Estimating the per-exposure effect of infectious disease interventions.

The average effect of an infectious disease intervention (eg, a vaccine) varies across populations with different degrees of exposure to the pathogen. As a result, many investigators favor a per-exposure effect measure that is considered independent of the population level of exposure and that can be used in simulations to estimate the total disease burden averted by an intervention across different populations. However, while per-exposure effects are frequently estimated, the quantity of interest is often poorly defined, and assumptions in its calculation are typically left implicit. In this article, we build upon work by Halloran and Struchiner (Epidemiology. 1995;6:142-151) to develop a formal definition of the per-exposure effect and discuss conditions necessary for its unbiased estimation. With greater care paid to the parameterization of transmission models, their results can be better understood and can thereby be of greater value to decision-makers.

Oseltamivir for treatment and prevention of pandemic influenza A/H1N1 virus infection in households, Milwaukee, 2009.

BACKGROUND: During an influenza pandemic, a substantial proportion of transmission is thought to occur in households. We used data on influenza progression in individuals and their contacts collected by the City of Milwaukee Health Department (MHD) to study the transmission of pandemic influenza A/H1N1 virus in 362 households in Milwaukee, WI, and the effects of oseltamivir treatment and chemoprophylaxis. METHODS: 135 households had chronological information on symptoms and oseltamivir usage for all household members. The effect of oseltamivir treatment and other factors on the household secondary attack rate was estimated using univariate and multivariate logistic regression with households as the unit of analysis. The effect of oseltamivir treatment and other factors on the individual secondary attack rate was estimated using univariate and multivariate logistic regression with individual household contacts as the unit of analysis, and a generalized estimating equations approach was used to fit the model to allow for clustering within households. RESULTS: Oseltamivir index treatment on onset day or the following day (early treatment) was associated with a 42% reduction (OR: 0.58, 95% CI: 0.19, 1.73) in the odds of one or more secondary infections in a household and a 50% reduction (OR: 0.5, 95% CI: 0.17, 1.46) in the odds of a secondary infection in individual contacts. The confidence bounds are wide due to a small sample of households with early oseltamivir index usage - in 29 such households, 5 had a secondary attack. Younger household contacts were at higher risk of infection (OR: 2.79, 95% CI: 1.50-5.20). CONCLUSIONS: Early oseltamivir treatment may be beneficial in preventing H1N1pdm influenza transmission; this may have relevance to future control measures for influenza pandemics. Larger randomized trials are needed to confirm this finding statistically.