Healthcare Worker Staffing Ratios Affect Methicillin-Resistant Staphylococcus aureus Acquisition.

Importance: This study addresses the pressing clinical question of how variations in physician and nursing staffing levels influence methicillin-resistant Staphylococcus aureus (MRSA) rates, providing essential insights for optimizing staff allocation and improving patient outcomes in critical care settings. Objective: The main objective is to assess whether variations in staffing ratios and workload conceptualization significantly alter the rates of MRSA acquisitions in the ICU setting. Design: This simulation-based study utilizes stochastic compartmental mathematical modeling to explore the impact of staffing ratios and workload conceptualization on MRSA acquisitions in ICUs. Derived from a previously published model, the analysis involves running year-long stochastic simulations for each scenario 1000 times, varying nurse-to-patient ratios and intensivist staffing levels under infinite and finite workload conceptualizations. Our baseline model was a 3:1 nurse ratio with one intensivist. Main Outcome: MRSA acquisitions in ICUs, measured as median acquisitions per 1000 person-years. Results: Under baseline conditions, our model had a median of 8.2 MRSA acquisitions per 1000 person-years. Varying patient-to-nurse ratios and intensivist numbers showed substantial impacts. For infinite models, a 2:1 nurse ratio resulted in a 21% decrease, while a 1:1 nurse ratio led to a 65% reduction. Finite models demonstrated even larger effects, with a 48% decrease when having a 2:1 ratio, and an 83% reduction with a 1:1 nurse ratio. Reducing patient-to-nurse ratios in finite models increased acquisitions exponentially with a 348% increase for a 6:1 ratio. Intensivist variations had modest impacts. Conclusions and Relevance: Our study highlights the crucial role of optimizing staffing levels in ICUs for effective MRSA infection control. While intensivist variations have modest effects, bolstering nursing ratios significantly reduces MRSA acquisitions, underscoring the need for tailored staffing strategies, and recognizing the nuanced impact of workload conceptualization. Our findings offer practical insights for refining staffing protocols, emphasizing the dynamic nature of healthcare-associated infection outcomes.

Estimating SARS-CoV-2 transmission parameters between coinciding outbreaks in a university population and the surrounding community.

Prior studies suggest that population heterogeneity in SARS-CoV-2 (COVID-19) transmission plays an important role in epidemic dynamics. During the fall of 2020, many US universities and the surrounding communities experienced an increase in reported incidence of SARS-CoV-2 infections, with a high disease burden among students. We explore the transmission dynamics of an outbreak of SARS-CoV-2 among university students, how it impacted the non-student population via cross-transmission, and how it could influence pandemic planning and response. Using surveillance data of reported SARS-CoV-2 cases, we developed a two-population SEIR model to estimate transmission parameters and evaluate how these subpopulations interacted during the 2020 Fall semester. We estimated the transmission rate among the university students (ßU) and community residents (ßC), as well as the rate of cross-transmission between the two subpopulations (ßM) using particle Markov Chain Monte Carlo (pMCMC) simulation-based methods. We found that both populations were more likely to interact with others in their population and that cross-transmission was minimal. The cross-transmission estimate (ßM) was considerably smaller [0.04 × 10-5 (95% CI: 0.00 × 10-5, 0.15 × 10-5)] compared to the community estimate (ßC) at 2.09 × 10-5 (95% CI: 1.12 × 10-5, 2.90 × 10-5) and university estimate (ßU) at 27.92 × 10-5 (95% CI: 19.97 × 10-5, 39.15 × 10-5). The higher within population transmission rates among the university and the community (698 and 52 times higher, respectively) when compared to the cross-transmission rate, suggests that these two populations did not transmit between each other heavily, despite their geographic overlap. During the first wave of the pandemic, two distinct epidemics occurred among two subpopulations within a relatively small US county population where university students accounted for roughly 41% of the total population. Transmission parameter estimates varied substantially with minimal or no cross-transmission between the subpopulations. Assumptions that county-level and other small populations are well-mixed during a respiratory viral pandemic should be reconsidered. More granular models reflecting overlapping subpopulations may assist with better-targeted interventions for local public health and healthcare facilities.

Examining the impact of ICU population interaction structure on modeled colonization dynamics of Staphylococcus aureus.

BACKGROUND: Complex transmission models of healthcare-associated infections provide insight for hospital epidemiology and infection control efforts, but they are difficult to implement and come at high computational costs. Structuring more simplified models to incorporate the heterogeneity of the intensive care unit (ICU) patient-provider interactions, we explore how methicillin-resistant Staphylococcus aureus (MRSA) dynamics and acquisitions may be better represented and approximated. METHODS: Using a stochastic compartmental model of an 18-bed ICU, we compared the rates of MRSA acquisition across three ICU population interaction structures: a model with nurses and physicians as a single staff type (SST), a model with separate staff types for nurses and physicians (Nurse-MD model), and a Metapopulation model where each nurse was assigned a group of patients. The proportion of time spent with the assigned patient group (γ) within the Metapopulation model was also varied. RESULTS: The SST, Nurse-MD, and Metapopulation models had a mean of 40.6, 32.2 and 19.6 annual MRSA acquisitions respectively. All models were sensitive to the same parameters in the same direction, although the Metapopulation model was less sensitive. The number of acquisitions varied non-linearly by values of γ, with values below 0.40 resembling the Nurse-MD model, while values above that converged toward the Metapopulation structure. DISCUSSION: Inclusion of complex population interactions within a modeled hospital ICU has considerable impact on model results, with the SST model having more than double the acquisition rate of the more structured metapopulation model. While the direction of parameter sensitivity remained the same, the magnitude of these differences varied, producing different colonization rates across relatively similar populations. The non-linearity of the model's response to differing values of a parameter gamma (γ) suggests simple model approximations are appropriate in only a narrow space of relatively dispersed nursing assignments. CONCLUSION: Simplifying assumptions around how a hospital population is modeled, especially assuming random mixing, may overestimate infection rates and the impact of interventions. In many, if not most, cases more complex models that represent population mixing with higher granularity are justified.

Transient dynamics of infection transmission in a simulated intensive care unit.

Healthcare-associated infections (HAIs) remain a serious public health problem. In previous work, two models of an intensive care unit (ICU) showed that differing population structures had markedly different rates of Staphylococcus aureus (MRSA) transmission. One explanation for this difference is the models having differing long-term equilbrium dynamics, resulting from different basic reproductive numbers, R0. We find in this system however that this is not the case, and that both models had the same value for R0. Instead, short-term, transient dynamics, characterizing a series of small, self-limiting outbreaks caused by pathogen reintroduction were responsible for the differences. These results show the importance of these short-term factors for disease systems where reintroduction events are frequent, even if they are below the epidemic threshold. Further, we examine how subtle changes in how a hospital is organized-or how a model assumes a hospital is organized-in terms of the admission of new patients may impact transmission rates. This has implications for both novel pathogens introduced into ICUs, such as Ebola, MERS or COVID-19, as well as existing healthcare-associated infections such as carbapenem-resistant Enterobacteriaceae.

Excess Risk of COVID-19 to University Populations Resulting from In-Person Sporting Events.

BACKGROUND: One of the consequences of COVID-19 has been the cancelation of collegiate sporting events. We explore the impact of sports on COVID-19 transmission on a college campus. METHODS: Using a compartmental model representing the university, we model the impact of influxes of 10,000 visitors attending events and ancillary activities (dining out, visiting family, shopping, etc.) on 20,000 students. We vary the extent visitors interact with the campus, the number of infectious visitors, and the extent to which the campus has controlled COVID-19 absent events. We also conduct a global sensitivity analysis. RESULTS: Events caused an increase in the number of cases ranging from a 25% increase when the campus already had an uncontrolled COVID-19 outbreak and visitors had a low prevalence of COVID-19 and mixed lightly with the campus community to an 822% increase where the campus had controlled their COVID-19 outbreak and visitors had both a high prevalence of COVID-19 and mixed heavily with the campus community. The model was insensitive to parameter uncertainty, save for the duration a symptomatic individual was infectious. CONCLUSION: Sporting events represent a threat to the health of the campus community. This is the case even in circumstances where COVID-19 seems controlled both on-campus and among the general population.

Estimated Methicillin-Resistant Staphylococcus aureus Decolonization in Intensive Care Units Associated With Single-Application Chlorhexidine Gluconate or Mupirocin.

Importance: Chlorhexidine gluconate (CHG) and mupirocin are widely used to decolonize patients with methicillin-resistant Staphylococcus aureus (MRSA) and reduce risks associated with infection in hospitalized populations. Quantifying the association of an application of CHG alone or in combination with mupirocin with risk of MRSA infection is important for studies evaluating alternative decolonization strategies or schedules and for identifying whether there is room for improved decolonizing agents. Objective: To estimate the proportion of patients with MRSA decolonized per application of CHG and mupirocin from existing population-level studies. Design, Setting, and Participants: A stochastic mathematical model of an 18-bed intensive care unit (ICU) in an academic medical center operating over 1 year was used to estimate parameters for the proportion of simulated patients with MRSA decolonized per application of CHG and mupirocin. The model was conducted using approximate bayesian computation with data from an existing meta-analysis of studies conducted from February 2005 through January 2015. Data were analyzed from January 2018 through November 2019. Exposure: A universal decolonization protocol for colonized patients in the ICU using CHG or CHG and mupirocin in combination was simulated. Main Outcomes and Measures: The proportion of patients with MRSA decolonized per application of CHG and mupirocin was estimated. Results: The estimated proportion of patients with MRSA decolonized per application of CHG was 0.15 (95% credible interval, 0.01-0.42), and the estimated proportion per application of mupirocin in conjunction with CHG was 0.15 (95% credible interval, 0.01-0.54). A lag in colonization detection was associated with decreases in the CHG estimate (0.11; 95% credible interval, 0.01-0.30) and mupirocin estimate (0.10; 95% credible interval, 0.00-0.34), which were sensitive to the value of the modeled contact rate between nurses and patients. A 1% increase in the value of this parameter was associated with a 0.73% increase in the estimated combined outcomes associated with CHG and mupirocin (95% CI: 0.71, 0.75). Gaps longer than 24 hours in the administration of decolonizing agents were associated with a decrease of within-ICU MRSA transmission. Compared with a mean (SD) of 1.23 (0.27) acquisitions per 1000 patient-days in scenarios with no decolonizing bathing, a bathing protocol administering CHG and mupirocin every 120 hours was associated with a mean (SD) acquisition rate of 1.03 (0.24) acquisitions per 1000 patient days, a 16.3% decrease (95% CI, 14.7%-18.0%; P > .001). Conclusions and Relevance: These findings suggest that there may be room for significant improvement in anti-MRSA disinfectants, including the compounds themselves and their delivery mechanisms. Despite the decolonization estimates found in this study, these agents are associated with robust outcomes after delays in administration, which may help in alleviating concerns over patient comfort and toxic effects.

Assessing the Potential Impact of a Long-Acting SkinDisinfectant in the Prevention of Methicillin-Resistant Staphylococcus aureus Transmission.

Healthcare-associated transmission of methicillin-resistant Staphylococcus aureus (MRSA)remains a persistent problem. The use of chlorhexidine gluconate (CHG) as a means of decolonizingpatients, either through targeted decolonization or daily bathing, is frequently used to supplementother interventions. We explore the potential of a long-acting disinfectant with a persistent effect,immediate decolonizing action in the prevention of MRSA acquisition, and clinical illness andmortality in an 18-bed intensive care unit, based on a previous model. A scenario with nointervention is compared to CHG bathing, which decolonizes patients but provides no additionalprotection, and a hypothetical treatment that both decolonizes them and provides protection fromsubsequent colonization. The duration and effectiveness of this protection is varied to fully explorethe potential utility of such a treatment. Increasing the effectiveness of the decolonizing agentreduces colonization, with a 10% increase resulting in a colonization rate ratio (RR) of 0.89 (95% CI:0.89,0.90). Increasing the duration of protection results in a much more modest reduction, with a 12-hour increase in protection resulting in an RR of 0.99 (95% CI: 0.99, 0.99). There is little evidence ofsynergy between the two.

Increased Gonorrhea Cases - Utah, 2009-2014.

Gonorrhea (caused by infection with Neisseria gonorrhoeae) is the second most commonly reported notifiable disease in the United States (1). Left untreated, gonorrhea is associated with serious long-term adverse health effects, including pelvic inflammatory disease, ectopic pregnancy, and infertility. Infection also facilitates transmission of human immunodeficiency virus (2,3). Effective gonorrhea control relies upon early detection and effective antimicrobial treatment. To assess gonorrhea rate trends in Utah, the Utah Department of Health (UDOH) analyzed Utah National Electronic Disease Surveillance System (UT-NEDSS) data for the state during 2009-2014. After declining during 2009-2011, the statewide gonorrhea rate increased fivefold to 49 cases per 100,000 population in 2014. During 2009-2014, the proportion of cases among women increased from 21% to 39% (decreasing among males from 79% to 61%). Among male patients, the proportion who identified as men who have sex with men (MSM) decreased from 67% to 42%. These demographic changes suggest that increased heterosexual transmission of gonorrhea in Utah might be occurring. Health departments need to work with providers to ensure populations at high risk are being screened and properly treated for gonorrhea. Clinicians need to be aware of increases in the risk for infection among women and non-MSM males when making screening and testing decisions and educate their patients regarding gonorrhea transmission and prevention practices.

Comparison of HIV/AIDS rates between U.S.-born Blacks and African-born Blacks in Utah, 2000 - 2009.

The Utah Department of Health currently groups African-born blacks with U.S.-born blacks when reporting HIV/AIDS surveillance data. Studies suggest that categorizing HIV/AIDS cases in this manner may mask important epidemiological trends, and the distinct differences between these two populations warrant disaggregating data prior to reporting. The purpose of this study was to characterize the HIV/AIDS positive populations in U.S. and African-born blacks in Utah and evaluate the need for disaggregating the two groups. A total of 1,111 cases were identified through the statewide electronic HIV/AIDS Reporting System from 2000 - 2009. Data were analyzed for prevalence of HIV diagnosis for African-born blacks, U.S.-born blacks, and U.S.-born whites. Secondary analysis included HIV diagnosis by age, sex, African region of nativity, transmission risk factors, and differences in late diagnosis of HIV infection. U.S.-born whites accounted for 914 (82.3%) cases, and had the lowest annual prevalence (4/100,000). Conversely, African-born and U.S.- born blacks had the highest prevalence, 162/100,000 and 24/100,000 respectively. African-born blacks made up 0.25% of the total population, but accounted for 7.9% of all HIV/AIDS cases. African-born black males were more likely to report "no reported risk" for HIV transmission than U.S.-born black males. Of African-born blacks, 55.7% reported East-African nativity. These results demonstrate the importance of stratifying the black/African American racial category by African-born and U.S.-born blacks when collecting and reporting HIV/AIDS state surveillance data even in a low-incidence state,which will better inform prevention and linkage-to-care efforts in Utah.