Estimate the risk for household transmission of Methicillin-Resistant Staphylococcus aureus (MRSA) following exposure to infected family members or family members recently discharged from a hospital.

Analysis of monthly MRSA incidence from longitudinal insurance claims using the Merative MarketScan Commercial and Medicare (2001–2021) databases.

Visits to inpatient, emergency department, and outpatient settings.

Households with ≥2 family members enrolled in the same insurance plan for the entire month.

We estimated a monthly incidence model, where enrollees were binned into monthly enrollment strata defined by demographic, patient, and exposure characteristics. Monthly incidence within each stratum was computed, and a regression analysis was used to estimate the incidence rate ratio (IRR) associated with household exposures of interest while accounting for potential confounding factors.

A total of 157,944,708 enrollees were included and 424,512 cases of MRSA were identified. Across all included enrollees, exposure to a family member with MRSA in the prior 30 days was associated with significantly increased risk of infection (IRR: 71.03 [95% CI, 67.73–74.50]). After removing enrollees who were hospitalized or exposed to a family member with MRSA, exposure to a family member who was recently discharged from the hospital was associated with increased risk of infection (IRR: 1.44 [95% CI, 1.39–1.49]) and the risk of infection increased with the duration of the family member’s hospital stay (P value < .001).

Exposure to a recently hospitalized and discharged family member increased the risk of MRSA infection in a household even when the hospitalized family member was not diagnosed with MRSA.

Compare the effectiveness of multiple mitigation measures designed to protect nursing home residents from infectious disease outbreaks.

Agent-based simulation study.

Simulation environment of a small nursing home.

We collected temporally detailed and spatially fine-grained location information from nursing home healthcare workers (HCWs) using sensor motes. We used these data to power an agent-based simulation of a COVID-19 outbreak using realistic time-varying estimates of infectivity and diagnostic sensitivity. Under varying community prevalence and transmissibility, we compared the mitigating effects of (i) regular screening and isolation, (ii) inter-resident contact restrictions, (iii) reduced HCW presenteeism, and (iv) modified HCW scheduling.

Across all configurations tested, screening every other day and isolating positive cases decreased the attack rate by an average of 27% to 0.501 on average, while contact restrictions decreased the attack rate by an average of 35%, resulting in an attack rate of only 0.240, approximately half that of screening/isolation. Combining both interventions impressively produced an attack rate of only 0.029. Halving the observed presenteeism rate led to an 18% decrease in the attack rate, but if combined with screening every 6 days, the effect of reducing presenteeism was negligible. Altering work schedules had negligible effects on the attack rate.

Universal contact restrictions are highly effective for protecting vulnerable nursing home residents, yet adversely affect physical and mental health. In high transmission and/or high community prevalence situations, restricting inter-resident contact to groups of 4 was effective and made highly effective when paired with weekly testing.

To determine whether Clostridioides difficile infection (CDI) exhibits spatiotemporal interaction and clustering.

Retrospective observational study.

The University of Iowa Hospitals and Clinics.

This study included 1,963 CDI cases, January 2005 through December 2011.

We extracted location and time information for each case and ran the Knox, Mantel, and mean and maximum component size tests for time thresholds (T = 7, 14, and 21 days) and distance thresholds (D = 2, 3, 4, and 5 units; 1 unit = 5–6 m). All tests were implemented using Monte Carlo simulations, and random CDI cases were constructed by randomly permuting times of CDI cases 20,000 times. As a counterfactual, we repeated all tests on 790 aspiration pneumonia cases because aspiration pneumonia is a complication without environmental factors.

Results from the Knox test and mean component size test rejected the null hypothesis of no spatiotemporal interaction (P < .0001), for all values of T and D. Results from the Mantel test also rejected the hypothesis of no spatiotemporal interaction (P < .0003). The same tests showed no such effects for aspiration pneumonia. Our results from the maximum component size tests showed similar trends, but they were not consistently significant, possibly because CDI outbreaks attributable to the environment were relatively small.

Our results clearly show spatiotemporal interaction and clustering among CDI cases and none whatsoever for aspiration pneumonia cases. These results strongly suggest that environmental factors play a role in the onset of some CDI cases. However, our results are not inconsistent with the possibility that many genetically unrelated CDI cases occurred during the study period.

To estimate the burden of Clostridium difficile infections (CDIs) due to interfacility patient sharing at regional and hospital levels.

Retrospective observational study.

We used data from the Healthcare Cost and Utilization Project California State Inpatient Database (2005–2011) to identify 26,878,498 admissions and 532,925 patient transfers. We constructed a weighted, directed network among the hospitals by defining an edge between 2 hospitals to be the monthly average number of patients discharged from one hospital and admitted to another on the same day. We then used a network autocorrelation model to study the effect of the patient sharing network on the monthly average number of CDI cases per hospital, and we estimated the proportion of CDI cases attributable to the network.

We found that 13% (95% confidence interval [CI], 7.6%–18%) of CDI cases were due to diffusion through the patient-sharing network. The network autocorrelation parameter was estimated at 5.0 (95% CI, 3.0–6.9). An increase in the number of patients transferred into and/or an increased CDI rate at the hospitals from which those patients originated led to an increase in the number of CDIs in the receiving hospital.

A minority but substantial burden of CDI infections are attributable to hospital transfers. A hospital’s infection control may thus be nontrivially influenced by its neighboring hospitals. This work adds to the growing body of evidence that intervention strategies designed to minimize HAIs should be done at the regional rather than local level.

To determine whether hand hygiene adherence is influenced by peer effects and, specifically, whether the presence and proximity of other healthcare workers has a positive effect on hand hygiene adherence

An observational study using a sensor network.

A 20-bed medical intensive care unit at a large university hospital.

Hospital staff assigned to the medical intensive care unit.

We deployed a custom-built, automated, hand hygiene monitoring system that can (1) detect whether a healthcare worker has practiced hand hygiene on entering and exiting a patient’s room and (2) estimate the location of other healthcare workers with respect to each healthcare worker exiting or entering a room.

We identified a total of 47,694 in-room and out-of-room hand hygiene opportunities during the 10-day study period. When a worker was alone (no recent healthcare worker contacts), the observed adherence rate was 20.85% (95% confidence interval [CI], 19.78%–21.92%). In contrast, when other healthcare workers were present, observed adherence was 27.90% (95% CI, 27.48%–28.33%). This absolute increase was statistically significant (P < .01). We also found that adherence increased with the number of nearby healthcare workers but at a decreasing rate. These results were consistent at different times of day, for different measures of social context, and after controlling for possible confounding factors.

The presence and proximity of other healthcare workers is associated with higher hand hygiene rates. Furthermore, our results also indicate that rates increase as the social environment becomes more crowded, but with diminishing marginal returns.

Infect Control Hosp Epidemiol 2014;35(10):1277–1285

To explore how hand hygiene observer scheduling influences the number of events and unique individuals observed.

We deployed a mobile sensor network to capture detailed movement data for 6 categories of healthcare workers over a 2-week period.

University of Iowa Hospital and Clinic medical intensive care unit (ICU).

We recorded 33,721 time-stamped healthcare worker entries to and exits from patient rooms and considered each entry or exit to be an opportunity for hand hygiene. Architectural drawings were used to derive 4 optimal line-of-sight placements for observers. We ran simulations for different observer movement schedules, all with a budget of 1 hour of total observation time. We considered observation times of 1–15, 15–30, 30, and 60 minutes per station. We stochastically generated healthcare worker hand hygiene compliance on the basis of all data and recorded the total unit compliance as it would be reported by each simulated observer.

Considering a 60-minute total observation period, aggregate simulated observers captured 1.7% of the average total number of opportunities per day at best and 0.5% at worst. The 1–15-minute schedule captures, on average, 16% fewer events than does the 60-minute (ie, static) schedule, but it samples 17% more unique individuals. The 1–15-minute schedule also provides the best estimator of compliance for the duration of the shift, with a mean standard deviation of 17%, compared with 23% for the 60-minute schedule.

Our results show that observations are sensitive to different observers' schedules and suggest the importance of using data-driven approaches to schedule hand hygiene audits.

To assess healthcare personnel (HCP) perceptions regarding implementation of sensor-based electronic systems for automated hand hygiene adherence monitoring.

Using a mixed-methods approach, structured focus groups were designed to elicit quantitative and qualitative responses on familiarity, comfort level, and perceived impact of sensor-based hand hygiene adherence monitoring

A university hospital, a Veterans Affairs hospital, and a community hospital in the Midwest.

Focus groups were homogenous by HCP type, with separate groups held for leadership, midlevel management, and frontline personnel at each hospital.

Overall, 89 HCP participated in 10 focus groups. Levels of familiarity and comfort with electronic oversight technology varied by HCP type; when compared with frontline HCP, those in leadership positions were significantly more familiar with (P<.01) and more comfortable with (P<.01) the technology. The most common concerns cited by participants across groups included lack of accuracy in the data produced, such as the inability of the technology to assess the situational context of hand hygiene opportunities, and the potential punitive use of data produced. Across groups, HCP had decreased tolerance for electronic collection of spatial-temporal data, describing such oversight as Big Brother.

While substantial concerns were expressed by all types of HCP, participants' recommendations for effective implementation of electronic oversight technologies for hand hygiene monitoring included addressing accuracy issues before implementation and transparent communication with frontline HCP about the intended use of the data.

To use social network analysis to design more effective strategies for vaccinating healthcare workers against influenza.

An agent-based simulation.

A simulation based on a 700-bed hospital.

We first observed human contacts (defined as approach within approximately 0.9 m) performed by 15 categories of healthcare workers (eg, floor nurses, intensive care unit nurses, staff physicians, phlebotomists, and respiratory therapists). We then constructed a series of contact graphs to represent the social network of the hospital and used these graphs to run agent-based simulations to model the spread of influenza. A targeted vaccination strategy that preferentially vaccinated more “connected” healthcare workers was compared with other vaccination strategies during simulations with various base vaccination rates, vaccine effectiveness, probability of transmission, duration of infection, and patient length of stay.

We recorded 6,654 contacts by 148 workers during 606 hours of observations from January through December 2006. Unit clerks, X-ray technicians, residents and fellows, transporters, and physical and occupational therapists had the most contacts. When repeated contacts with the same individual were excluded, transporters, unit clerks, X-ray technicians, physical and occupational therapists, and social workers had the most contacts. Preferentially vaccinating healthcare workers in more connected job categories yielded a substantially lower attack rate and fewer infections than a random vaccination strategy for all simulation parameters.

Social network models can be used to derive more effective vaccination policies, which are crucial during vaccine shortages or in facilities with low vaccination rates. Local vaccination priorities can be determined in any healthcare facility with only a modest investment in collection of observational data on different types of healthcare workers. Our findings and methods (ie, social network analysis and computational simulation) have implications for the design of effective interventions to control a broad range of healthcare-associated infections.