Modeling hospital energy and economic costs for COVID-19 infection control interventions.

The study objective assessed the energy demand and economic cost of two hospital-based COVID-19 infection control interventions: negative pressure (NP) treatment rooms and xenon pulsed ultraviolet (XP-UV) equipment. After projecting COVID-19 hospitalizations, a Hospital Energy Model and Infection De-escalation Models quantified increases in energy demand and reductions in infections. The NP intervention was applied to 11, 22, and 44 rooms for small, medium, and large hospitals, while the XP-UV equipment was used eight, nine, and ten hours a day. For small, medium, and large hospitals, the annum kWh for NP rooms were 116,700 kWh, 332,530 kWh, 795,675 kWh, which correspond to annum energy costs of $11,845 ($1,077/room), $33,752 ($1,534/room), and $80,761 ($1,836/room). For XP-UV, the annum-kilowatt-hours (and costs) were 438 ($45), 493 ($50), and 548 ($56) for small, medium, and large hospitals. While energy efficiencies may be expected for the large hospital, the hospital contained more energy-intensive use rooms (ICUs) which resulted in higher operational and energy costs. XP-UV had a greater reduction in secondary COVID-19 infections in large and medium hospitals. NP rooms had a greater reduction in secondary SARS-CoV-2 transmission in small hospitals. Early implementation of interventions can result in realized cost savings through reduced hospital-acquired infections.

Optimal Design of Paired Built Environment Interventions for Control of MDROs in Acute Care and Community Hospitals.

OBJECTIVES: Our goal was to optimize infection control of paired environmental control interventions within hospitals to reduce methicillin-resistant Staphylococcus aureus (MRSA), carbapenem-resistant Enterobacteriaceae (CRE), and vancomycin-resistant Enterococci (VRE). BACKGROUND: The most widely used infection control interventions are deployment of handwashing (HW) stations, control of relative humidity (RH), and negative pressure (NP) treatment rooms. Direct costs of multidrug-resistant organism (MDRO) infections are typically not included in the design of such interventions. METHODS: We examined the effectiveness of pairing HW with RH and HW with NP. We used the following three data sets: A meta-analysis of progression rates from uncolonized to colonized to infected, 6 years of MDRO treatment costs from 400 hospitals, and 8 years of MDRO incidence rates at nine army hospitals. We used these data as inputs into an Infection De-Escalation Model with varying budgets to obtain optimal intervention designs. We then computed the infection and prevention rates and cost savings resulting from these designs. RESULTS: The average direct cost of an MDRO infection was $3,289, $1,535, and $1,067 for MRSA, CRE, and VRE. The mean annual incidence rates per facility were 0.39%, 0.034%, and 0.011% for MRSA, CRE, and VRE. After applying the cost-minimizing intervention pair to each scenario, the percentage reductions in infections (and annual direct cost savings) in large, community, and small acute care hospitals were 69% ($1.5 million), 73% ($631K), 60% ($118K) for MRSA, 52% ($460.5K), 58% ($203K), 50% ($37K) for CRE, and 0%, 0%, and 50% ($12.8K) for VRE. CONCLUSION: The application of this Infection De-Escalation Model can guide cost-effective decision making in hospital built environment design to improve control of MDRO infections.

Association between mobility patterns and COVID-19 transmission in the USA: a mathematical modelling study.

BACKGROUND: Within 4 months of COVID-19 first being reported in the USA, it spread to every state and to more than 90% of all counties. During this period, the US COVID-19 response was highly decentralised, with stay-at-home directives issued by state and local officials, subject to varying levels of enforcement. The absence of a centralised policy and timeline combined with the complex dynamics of human mobility and the variable intensity of local outbreaks makes assessing the effect of large-scale social distancing on COVID-19 transmission in the USA a challenge. METHODS: We used daily mobility data derived from aggregated and anonymised cell (mobile) phone data, provided by Teralytics (Zürich, Switzerland) from Jan 1 to April 20, 2020, to capture real-time trends in movement patterns for each US county, and used these data to generate a social distancing metric. We used epidemiological data to compute the COVID-19 growth rate ratio for a given county on a given day. Using these metrics, we evaluated how social distancing, measured by the relative change in mobility, affected the rate of new infections in the 25 counties in the USA with the highest number of confirmed cases on April 16, 2020, by fitting a statistical model for each county. FINDINGS: Our analysis revealed that mobility patterns are strongly correlated with decreased COVID-19 case growth rates for the most affected counties in the USA, with Pearson correlation coefficients above 0·7 for 20 of the 25 counties evaluated. Additionally, the effect of changes in mobility patterns, which dropped by 35-63% relative to the normal conditions, on COVID-19 transmission are not likely to be perceptible for 9-12 days, and potentially up to 3 weeks, which is consistent with the incubation time of severe acute respiratory syndrome coronavirus 2 plus additional time for reporting. We also show evidence that behavioural changes were already underway in many US counties days to weeks before state-level or local-level stay-at-home policies were implemented, implying that individuals anticipated public health directives where social distancing was adopted, despite a mixed political message. INTERPRETATION: This study strongly supports a role of social distancing as an effective way to mitigate COVID-19 transmission in the USA. Until a COVID-19 vaccine is widely available, social distancing will remain one of the primary measures to combat disease spread, and these findings should serve to support more timely policy making around social distancing in the USA in the future. FUNDING: None.

Cost-Effectiveness of Multifaceted Built Environment Interventions for Reducing Transmission of Pathogenic Bacteria in Healthcare Facilities.

OBJECTIVES: The objective of this study is to determine the optimal allocation of budgets for pairs of alterations that reduce pathogenic bacterial transmission. Three alterations of the built environment are examined: handwashing stations (HW), relative humidity control (RH), and negatively pressured treatment rooms (NP). These interventions were evaluated to minimize total cost of healthcare-associated infections (HAIs), including medical and litigation costs. BACKGROUND: HAIs are largely preventable but are difficult to control because of their multiple mechanisms of transmission. Moreover, the costs of HAIs and resulting mortality are increasing with the latest estimates at US$9.8 billion annually. METHOD: Using 6 years of longitudinal multidrug-resistant infection data, we simulated the transmission of pathogenic bacteria and the infection control efforts of the three alterations using Chamchod and Ruan's model. We determined the optimal budget allocations among the alterations by representing them under Karush-Kuhn-Tucker conditions for this nonlinear optimization problem. RESULTS: We examined 24 scenarios using three virulence levels across three facility sizes with varying budget levels. We found that in general, most of the budget is allocated to the NP or RH alterations in each intervention. At lower budgets, however, it was necessary to use the lower cost alterations, HW or RH. CONCLUSIONS: Mathematical optimization offers healthcare enterprise executives and engineers a tool to assist with the design of safer healthcare facilities within a fiscally constrained environment. Herein, models were developed for the optimal allocation of funds between HW, RH, and negatively pressured treatment rooms (NP) to best reduce HAIs. Specific strategies vary by facility size and virulence.