Comparing multiple infection control measures in a nursing home setting: a simulation study.

OBJECTIVE: Compare the effectiveness of multiple mitigation measures designed to protect nursing home residents from infectious disease outbreaks. DESIGN: Agent-based simulation study. SETTING: Simulation environment of a small nursing home. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Ecological momentary assessment of pelvic pain and urinary urgency variability in urologic chronic pelvic pain syndrome and their association with illness impact and quality of life: Findings from the multidisciplinary approach to the study of chronic pelvic pain symptom patterns study.

PURPOSE: This study tested the hypothesis that ecological momentary assessment (EMA) of pelvic pain (PP) and urinary urgency (UU) would reveal unique Urologic Chronic Pelvic Pain Syndrome (UCPPS) phenotypes that would be associated with disease specific quality of life (QOL) and illness impact metrics (IIM). MATERIALS AND METHODS: A previously validated smart phone app (M-app) was provided to willing Multidisciplinary Approach to the Study of Chronic Pelvic Pain (MAPP) participants. M-app notifications were sent 4-times daily for 14 days inquiring about PP and UU severity. A clustering algorithm that accounted for variance placed participants into PP and UU variability? clusters. Associations between clusters and QOL and IIM were then determined. RESULTS: A total of 204 participants enrolled in the M-app study (64% female). M-app compliance was high (median 63% of surveys). Cluster analysis revealed k = 3 (high, low, none) PP clusters and k = 2 (high, low) UU clusters. When adjusting for baseline pain severity, high PP variability, but not UU variability, was strongly associated with QOL and IIM; specifically worse mood, worse sleep and higher anxiety. UU and PP clusters were associated with each other (p < 0.0001), but a large percentage (33%) of patients with high PP variability had low UU variability. CONCLUSIONS: PP variability is an independent predictor of worse QOL and more severe IIM in UCPPS participants after controlling for baseline pain severity and UU. These findings suggest alternative pain indices, such as pain variability and unpredictability, may be useful adjuncts to traditional measures of worst and average pain when assessing UCPPS treatment responses.

A Mobile Phone Application for Assessing Daily Variation in Pain Location and Pain Intensity in Patients with Urologic Chronic Pelvic Pain Syndrome: A MAPP Network Study.

Introduction: We created and tested a mobile app that facilitates the ecological momentary assessment of pain intensity and pain location and identifies heterogeneous patient pain phenotypes. Methods: A mobile app was created with patient, clinician and researcher input. A sample of 20 participants with urologic chronic pelvic pain syndrome were then asked to complete a 14-day pain assessment using the app. Data were analyzed to assess compliance, usability and the ability for the app to capture variation in pain intensity and pain location. Ecological momentary assessment pain data were then compared to end-of-week pain summary questions to determine construct validity. Results: Mean compliance was 70±8%, higher earlier in the study period (p <0.0005) and better in older individuals (p <0.0001). During the 14-day assessment, 90% of participants reported daily variation in pelvic pain intensity (SD 0.64-3.02; out of 10), 95% reported variation in their nonpelvic pain (SD 0.17-3.63; out of 10) and 100% reported variations in number of sites with pain (SD 0.22-1.44; out of 7). Pelvic pain and nonpelvic pain intensity, as determined by cumulative app scores, were associated with patient reported end-of-week scores; worst pain (r pelvic =0.67; r nonpelvic =0.53) and average pain (r pelvic =0.78; r nonpelvic =0.73). Conclusions: The easy-to-use app captured unique patterns of pain not fully captured by traditional end-of-day/week summary questions or by traditional in-office assessments. Mobile apps for assessing chronic conditions will become increasingly important as telehealth becomes more commonplace.

Continuous in-home PM2.5 concentrations of smokers with and without a history of respiratory exacerbations in Iowa, during and after an air purifier intervention.

BACKGROUND: Americans spend most of their time indoors. Indoor particulate matter (PM) 2.5 µm and smaller (PM2.5) concentrations often exceed ambient concentrations. Therefore, we tested whether the use of an air purifying device (electrostatic precipitator, ESP) could reduce PM2.5 in homes of smokers with and without respiratory exacerbations, compared with baseline. METHODS: We assessed PM2.5 concentrations in homes of subjects with and without a recent (≤3 years) history of respiratory exacerbation. We compared PM2.5 concentrations during 1 month of ESP use with those during 1 month without ESP use. RESULTS: Our study included 19 subjects (53-80 years old), nine with a history of respiratory exacerbation. Geometric mean (GM) PM2.5 and median GM daily peak PM2.5 were significantly lower during ESP deployment compared with the equivalent time-period without the ESP (GSD = 0.50 and 0.37 µg/m3, respectively, p < 0.001). PM2.5 in homes of respiratory exacerbators tended (p < 0.14) to be higher than PM2.5 in homes of those without a history of respiratory exacerbation. CONCLUSIONS: Subjects with a history of respiratory exacerbation tended to have higher mean, median, and mean peak PM2.5 concentrations compared with homes of subjects without a history of exacerbations. The ESP intervention reduced in-home PM2.5 concentrations, demonstrating its utility in reducing indoor exposures. NOVELTY OF STUDY: Our work characterizes PM air pollution concentrations in homes of study subjects with and without respiratory exacerbations. We demonstrate that PM concentrations tend to be higher in homes of participants with respiratory exacerbations, and that the use of an inexpensive air purifier resulted in significantly lower daily average PM concentrations than when the purifier was not present. Our results provide a helpful intervention strategy for purifying indoor air and may be useful for susceptible populations.

Indoor Particulate Matter From Smoker Homes Induces Bacterial Growth, Biofilm Formation, and Impairs Airway Antimicrobial Activity. A Pilot Study.

Background: Particulate matter (PM) air pollution causes deleterious health effects; however, less is known about health effects of indoor air particulate matter (IAP). Objective: To understand whether IAP influences distinct mechanisms in the development of respiratory tract infections, including bacterial growth, biofilm formation, and innate immunity. Additionally, we tested whether IAP from Iowa houses of subjects with and without recent respiratory exacerbations recapitulated the National Institute of Standards and Technology (NIST) IAP findings. Methods: To test the effect of NIST and Iowa IAP on bacterial growth and biofilm formation, we assessed Staphylococcus aureus growth and Pseudomonas aeruginosa biofilm formation with and without the presence of IAP. To assess the effect of IAP on innate immunity, we exposed primary human airway surface liquid (ASL) to NIST, and Iowa IAP. Lastly, we tested whether specific metals may be responsible for effects on airway innate immunity. Results: NIST and Iowa IAP significantly enhanced bacterial growth and biofilm formation. NIST IAP (whole particle and the soluble portion) impaired ASL antimicrobial activity. IAP from one Iowa home significantly impaired ASL antimicrobial activity (p < 0.05), and five other homes demonstrated a trend (p ≤ 0.18) of impaired ASL antimicrobial activity. IAP from homes of subjects with a recent history of respiratory exacerbation tended (p = 0.09) to impair ASL antimicrobial activity more than IAP from homes of those without a history respiratory exacerbation. Aluminum and Magnesium impaired ASL antimicrobial activity, while copper was bactericidal. Combining metals varied their effect on ASL antimicrobial activity. Conclusions: NIST IAP and Iowa IAP enhanced bacterial growth and biofilm formation. ASL antimicrobial activity was impaired by NIST IAP, and Iowa house IAP from subjects with recent respiratory exacerbation tended to impair ASL antimicrobial activity. Individual metals may explain impaired ASL antimicrobial activity; however, antimicrobial activity in the presence of multiple metals warrants further study.

Estimating Time Physicians and Other Health Care Workers Spend with Patients in an Intensive Care Unit Using a Sensor Network.

BACKGROUND: Time and motion studies have been used to investigate how much time various health care professionals spend with patients as opposed to performing other tasks. However, the majority of such studies are done in outpatient settings, and rely on surveys (which are subject to recall bias) or human observers (which are subject to observation bias). Our goal was to accurately measure the time physicians, nurses, and critical support staff in a medical intensive care unit spend in direct patient contact, using a novel method that does not rely on self-report or human observers. METHODS: We used a network of stationary and wearable mote-based sensors to electronically record location and contacts among health care workers and patients under their care in a 20-bed intensive care unit for a 10-day period covering both day and night shifts. Location and contact data were used to classify the type of task being performed by health care workers. RESULTS: For physicians, 14.73% (17.96%) of their time in the unit during the day shift (night shift) was spent in patient rooms, compared with 40.63% (30.09%) spent in the physician work room; the remaining 44.64% (51.95%) of their time was spent elsewhere. For nurses, 32.97% (32.85%) of their time on unit was spent in patient rooms, with an additional 11.34% (11.79%) spent just outside patient rooms. They spent 11.58% (13.16%) of their time at the nurses' station and 23.89% (24.34%) elsewhere in the unit. From a patient's perspective, we found that care times, defined as time with at least one health care worker of a designated type in their intensive care unit room, were distributed as follows: 13.11% (9.90%) with physicians, 86.14% (88.15%) with nurses, and 8.14% (7.52%) with critical support staff (eg, respiratory therapists, pharmacists). CONCLUSIONS: Physicians, nurses, and critical support staff spend very little of their time in direct patient contact in an intensive care unit setting, similar to reported observations in both outpatient and inpatient settings. Not surprisingly, nurses spend far more time with patients than physicians. Additionally, physicians spend more than twice as much time in the physician work room (where electronic medical record review and documentation occurs) than the time they spend with all of their patients combined.

Do peer effects improve hand hygiene adherence among healthcare workers?

OBJECTIVE: 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. DESIGN: An observational study using a sensor network. SETTING: A 20-bed medical intensive care unit at a large university hospital. PARTICIPANTS: Hospital staff assigned to the medical intensive care unit. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Electronic recognition of hand hygiene technique and duration.

We captured 3-dimensional accelerometry data from the wrists of 116 healthcare professionals as they performed hand hygiene (HH). We then used these data to train a k-nearest-neighbors classifier to recognize specific aspects of HH technique (ie, fingertip scrub) and measure the duration of HH events.

Using sensor networks to study the effect of peripatetic healthcare workers on the spread of hospital-associated infections.

BACKGROUND: Super-spreading events, in which an individual with measurably high connectivity is responsible for infecting a large number of people, have been observed. Our goal is to determine the impact of hand hygiene noncompliance among peripatetic (eg, highly mobile or highly connected) healthcare workers compared with less-connected workers. METHODS: We used a mote-based sensor network to record contacts among healthcare workers and patients in a 20-bed intensive care unit. The data collected from this network form the basis for an agent-based simulation to model the spread of nosocomial pathogens with various transmission probabilities. We identified the most- and least-connected healthcare workers. We then compared the effects of hand hygiene noncompliance as a function of connectedness. RESULTS: The data confirm the presence of peripatetic healthcare workers. Also, agent-based simulations using our real contact network data confirm that the average number of infected patients was significantly higher when the most connected healthcare worker did not practice hand hygiene and significantly lower when the least connected healthcare workers were noncompliant. CONCLUSIONS: Heterogeneity in healthcare worker contact patterns dramatically affects disease diffusion. Our findings should inform future infection control interventions and encourage the application of social network analysis to study disease transmission in healthcare settings.

Monitoring hand hygiene via human observers: how should we be sampling?

OBJECTIVE: To explore how hand hygiene observer scheduling influences the number of events and unique individuals observed. DESIGN: We deployed a mobile sensor network to capture detailed movement data for 6 categories of healthcare workers over a 2-week period. SETTING: University of Iowa Hospital and Clinic medical intensive care unit (ICU). METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.

Healthcare personnel perceptions of hand hygiene monitoring technology.

OBJECTIVE: To assess healthcare personnel (HCP) perceptions regarding implementation of sensor-based electronic systems for automated hand hygiene adherence monitoring. DESIGN: 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. SETTING: A university hospital, a Veterans Affairs hospital, and a community hospital in the Midwest. PARTICIPANTS: Focus groups were homogenous by HCP type, with separate groups held for leadership, midlevel management, and frontline personnel at each hospital. RESULTS: 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 ([Formula: see text]) and more comfortable with ([Formula: see text]) 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. CONCLUSIONS: 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.

Method for automated monitoring of hand hygiene adherence without radio-frequency identification.

Many efforts to automatically measure hand hygiene activity depend on radio-frequency identification equipment or similar technology that can be expensive to install. We have developed a method for automatically tracking the use of hand hygiene dispensers before healthcare workers enter (or after they exit) patient rooms that is easily and quickly deployed without permanent hardware.