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Article ; Online: Decolonization in Nursing Homes to Prevent Infection. Reply.

Miller, Loren G / Gussin, Gabrielle M / Huang, Susan S

2024 Volume 390, Issue 7, Page(s) 671–673

MeSH term(s)	Humans ; Cross Infection/prevention & control ; Staphylococcal Infections/prevention & control ; Nursing Homes ; Hospitalization
Language	English
Publishing date	2024-02-13
Publishing country	United States
Document type	Letter ; Comment
ZDB-ID	207154-x
ISSN	1533-4406 ; 0028-4793
ISSN (online)	1533-4406
ISSN	0028-4793
DOI	10.1056/NEJMc2315001
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Article ; Online: Community COVID-19 Incidence and Health Care Personnel COVID-19 Seroprevalence.

Gohil, Shruti K / Huang, Susan S

JAMA network open

2021 Volume 4, Issue 3, Page(s) e211575

MeSH term(s)	COVID-19 ; Delivery of Health Care ; Humans ; Incidence ; Risk Factors ; SARS-CoV-2 ; Seroepidemiologic Studies
Language	English
Publishing date	2021-03-01
Publishing country	United States
Document type	Journal Article ; Comment
ISSN	2574-3805
ISSN (online)	2574-3805
DOI	10.1001/jamanetworkopen.2021.1575
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Article ; Online: Catheter-Associated Urinary Tract Infections--Turning the Tide.

Huang, Susan S

The New England journal of medicine

2016 Volume 374, Issue 22, Page(s) 2168–2169

MeSH term(s)	Catheter-Related Infections/prevention & control ; Cross Infection/prevention & control ; Humans ; Urinary Catheterization/utilization ; Urinary Tract Infections/prevention & control
Language	English
Publishing date	2016-06-02
Publishing country	United States
Document type	Comment ; Editorial
ZDB-ID	207154-x
ISSN	1533-4406 ; 0028-4793
ISSN (online)	1533-4406
ISSN	0028-4793
DOI	10.1056/NEJMe1604647
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Article ; Online: Value of a confidential COVID-19 helpline for nursing home staff.

Gussin, Gabrielle M / Singh, Raveena D / Nguyen, Kristine P / Huang, Susan S

American journal of infection control

2022 Volume 51, Issue 7, Page(s) 841–843

Abstract: We hosted a confidential helpline to address concerns about COVID-19 prevention among staff in 12 nursing homes in Orange County, California. We fielded 301 inquiries from April 2021-April 2022, most commonly involving questions about vaccines (40%), ... ...

Abstract	We hosted a confidential helpline to address concerns about COVID-19 prevention among staff in 12 nursing homes in Orange County, California. We fielded 301 inquiries from April 2021-April 2022, most commonly involving questions about vaccines (40%), nursing home COVID-19 prevention (28%), SARS-CoV-2 variants (18%), symptom reporting (10%), and home and community COVID-19 prevention (5%). During COVID-19 surges, staff dominantly expressed fear, anger, and exhaustion. During nadirs, sentiment shifted towards optimism and acceptance.
MeSH term(s)	Humans ; COVID-19/prevention & control ; SARS-CoV-2 ; Nursing Homes ; Skilled Nursing Facilities
Language	English
Publishing date	2022-11-18
Publishing country	United States
Document type	Journal Article ; Research Support, U.S. Gov't, P.H.S.
ZDB-ID	392362-9
ISSN	1527-3296 ; 0196-6553
ISSN (online)	1527-3296
ISSN	0196-6553
DOI	10.1016/j.ajic.2022.11.004
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Article ; Online: Matching in cluster randomized trials using the Goldilocks Approach.

Sturdevant, S Gwynn / Huang, Susan S / Platt, Richard / Kleinman, Ken

Contemporary clinical trials communications

2021 Volume 22, Page(s) 100746

Abstract: In group or cluster-randomized trials (GRTs), matching is a technique that can be used to improve covariate balance. When baseline data are available, we suggest a strategy that can be used to achieve the desired balance between treatment and control ... ...

Abstract	In group or cluster-randomized trials (GRTs), matching is a technique that can be used to improve covariate balance. When baseline data are available, we suggest a strategy that can be used to achieve the desired balance between treatment and control groups across numerous potential confounding variables. This strategy minimizes the overall within-pair Mahalanobis distance; and involves iteratively: 1) making pairs that minimize the distance between pairs of clusters with respect to potentially confounding variables; 2) visually assessing the potential effects of these pairs and resulting possible randomizations; and 3) reweighting variables of selecting weights to make pairs of clusters. In step 2, we plot the between-arm differences with a parallel-coordinates plot. Investigators can compare plots of different weighting schemes to determine the one that best suits their needs prior to the actual, final, randomization. We demonstrate application of the approach with the Mupirocin-Iodophor Swap Out trial. A webapp is provided.
Language	English
Publishing date	2021-05-05
Publishing country	Netherlands
Document type	Journal Article
ISSN	2451-8654
ISSN (online)	2451-8654
DOI	10.1016/j.conctc.2021.100746
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Article: Calculating Power by Bootstrap, with an Application to Cluster-Randomized Trials.

Kleinman, Ken / Huang, Susan S

EGEMS (Washington, DC)

2017 Volume 4, Issue 1, Page(s) 1202

Abstract: Background: A key requirement for a useful power calculation is that the calculation mimic the data analysis that will be performed on the actual data, once that data is observed. Close approximations may be difficult to achieve using analytic solutions, ...

Abstract	Background: A key requirement for a useful power calculation is that the calculation mimic the data analysis that will be performed on the actual data, once that data is observed. Close approximations may be difficult to achieve using analytic solutions, however, and thus Monte Carlo approaches, including both simulation and bootstrap resampling, are often attractive. One setting in which this is particularly true is cluster-randomized trial designs. However, Monte Carlo approaches are useful in many additional settings as well. Calculating power for cluster-randomized trials using analytic or simulation-based methods is frequently unsatisfactory due to the complexity of the data analysis methods to be employed and to the sparseness of data to inform the choice of important parameters in these methods. Methods: We propose that among Monte Carlo methods, bootstrap approaches are most likely to generate data similar to the observed data. In bootstrap approaches, real data are resampled to build complete data sets based on real data that resemble the data for the intended analyses. In contrast, simulation methods would use the real data to estimate parameters for the data, and would then simulate data using these parameters. We describe means of implementing bootstrap power calculation. Results: We demonstrate bootstrap power calculation for a cluster-randomized trial with a censored survival outcome and a baseline observation period. Conclusions: Bootstrap power calculation is a natural application of resampling methods. It provides a relatively simple solution to power calculation that is likely to be more accurate than analytic solutions or simulation-based calculations, in the sense that the bootstrap approach does not rely on the assumptions inherent in analytic calculations. This method of calculation has several important strengths. Notably, it is simple to achieve great fidelity to the proposed data analysis method and there is no requirement for parameter estimates, or estimates of their variability, from outside settings. So, for example, for cluster-randomized trials, power calculations need not depend on intracluster correlation coefficient estimates from outside studies. In contrast, bootstrap power calculation requires initial data that resemble data that are to be used in the planned study. We are not aware of bootstrap power calculation being previously proposed or explored for cluster-randomized trials, but it can also be applied for other study designs. We show with a simulation study that bootstrap power calculation can replicate analytic power in cases where analytic power can be accurately calculated. We also demonstrate power calculations for correlated censored survival outcomes in a cluster-randomized trial setting, for which we are unaware of an analytic alternative. The method can easily be used when preliminary data are available, as is likely to be the case when research is performed in health delivery systems or other settings where electronic medical records can be obtained.
Language	English
Publishing date	2017-02-09
Publishing country	England
Document type	Journal Article
ZDB-ID	2734659-6
ISSN	2327-9214
ISSN	2327-9214
DOI	10.13063/2327-9214.1202
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Article ; Online: Beyond Costs: Pragmatic Considerations for Pooling of SARS-CoV-2 Test Samples for Nursing Home Surveillance.

Gussin, Gabrielle M / Singh, Raveena D / Tifrea, Delia F / Tjoa, Thomas / Bittencourt, Cassiana E / Edwards, Robert A / Monuki, Edwin S / Huang, Susan S

Microbiology spectrum

2023 , Page(s) e0388022

Abstract: Pooling of samples can increase throughput and reduce costs for large-scale SARS-CoV-2 testing when incidence is low. In a cross-sectional study of serial SARS-CoV-2 sampling of staff and residents at three nursing homes, laboratory labor constraints ... ...

Abstract	Pooling of samples can increase throughput and reduce costs for large-scale SARS-CoV-2 testing when incidence is low. In a cross-sectional study of serial SARS-CoV-2 sampling of staff and residents at three nursing homes, laboratory labor constraints limited the feasibility of pooling prior to the maximal incidence that favored cost savings.
Language	English
Publishing date	2023-02-01
Publishing country	United States
Document type	Journal Article
ZDB-ID	2807133-5
ISSN	2165-0497 ; 2165-0497
ISSN (online)	2165-0497
ISSN	2165-0497
DOI	10.1128/spectrum.03880-22
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Article ; Online: National validation of the Centers for Medicare & Medicaid Services strategy for identifying potential surgical-site infections following colon surgery and abdominal hysterectomy.

Calderwood, Michael S / Kleinman, Ken / Bruce, Christina B / Shimelman, Lauren / Kaganov, Rebecca E / Platt, Richard / Huang, Susan S

Infection control and hospital epidemiology

2023 Volume 45, Issue 2, Page(s) 167–173

Abstract: Objective: National validation of claims-based surveillance for surgical-site infections (SSIs) following colon surgery and abdominal hysterectomy.: Design: Retrospective cohort study.: Setting: US hospitals selected for data validation by Centers ...

Abstract	Objective: National validation of claims-based surveillance for surgical-site infections (SSIs) following colon surgery and abdominal hysterectomy. Design: Retrospective cohort study. Setting: US hospitals selected for data validation by Centers for Medicare & Medicaid Services (CMS). Participants: The study included 550 hospitals performing colon surgery and 458 hospitals performing abdominal hysterectomy in federal fiscal year 2013. Methods: We requested 1,200 medical records from hospitals selected for validation as part of the CMS Hospital Inpatient Quality Reporting program. For colon surgery, we sampled 60% with a billing code suggestive of SSI during their index admission and/or readmission within 30 days and 40% who were readmitted without one of these codes. For abdominal hysterectomy, we included all patients with an SSI code during their index admission, all patients readmitted within 30 days, and a sample of those with a prolonged surgical admission (length of stay > 7 days). We calculated sensitivity and positive predictive value for the different groups. Results: We identified 142 colon-surgery SSIs (46 superficial SSIs and 96 deep and organ-space SSIs) and 127 abdominal-hysterectomy SSIs (58 superficial SSIs and 69 deep and organ-space SSIs). Extrapolating to the full CMS data validation cohort, we estimated an SSI rate of 8.3% for colon surgery and 3.0% for abdominal hysterectomy. Our colon-surgery surveillance codes identified 93% of SSIs, with 1 SSI identified for every 2.6 patients reviewed. Our abdominal-hysterectomy surveillance codes identified 73% of SSIs, with 1 SSI identified for every 1.6 patients reviewed. Conclusions: Using claims to target record review for SSI validation performed well in a national sample.
MeSH term(s)	Female ; Humans ; Aged ; United States/epidemiology ; Retrospective Studies ; Medicaid ; Medicare ; Surgical Wound Infection/diagnosis ; Surgical Wound Infection/epidemiology ; Colon/surgery ; Hysterectomy/adverse effects ; Risk Factors
Language	English
Publishing date	2023-09-07
Publishing country	United States
Document type	Journal Article
ZDB-ID	639378-0
ISSN	1559-6834 ; 0195-9417 ; 0899-823X
ISSN (online)	1559-6834
ISSN	0195-9417 ; 0899-823X
DOI	10.1017/ice.2023.193
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Article ; Online: Evaluating barriers and potential solutions to speaking up about coronavirus disease 2019 (COVID-19) symptoms: A survey among nursing home workers.

Gussin, Gabrielle M / Singh, Raveena D / Tjoa, Thomas T / Saavedra, Raheeb / Kaplan, Sherrie H / Huang, Susan S

Infection control and hospital epidemiology

2023 Volume 44, Issue 11, Page(s) 1834–1839

Abstract: Objective: Quantify the frequency and drivers of unreported coronavirus disease 2019 (COVID-19) symptoms among nursing home (NH) staff.: Design: Confidential telephone survey.: Setting: The study was conducted in 70 NHs in Orange County, ... ...

Abstract	Objective: Quantify the frequency and drivers of unreported coronavirus disease 2019 (COVID-19) symptoms among nursing home (NH) staff. Design: Confidential telephone survey. Setting: The study was conducted in 70 NHs in Orange County, California, December 2020-February 2022. Participants: The study included 120 NH staff with COVID-19. Methods: We designed a 40-item telephone survey of NH staff to assess COVID-19 symptom reporting behavior and types of barriers [monetary, logistic, and emotional (fear or stigma)] and facilitators of symptom reporting using 5-point Likert scales. Summary statistics, reliability of survey constructs, and construct and discriminant validity were assessed. Results: Overall, 49% of surveys were completed during the 2020-2021 COVID-19 winter wave and 51% were completed during severe acute respiratory coronavirus virus 2 (SARS-CoV-2) δ (delta)/ (omicron) waves, with a relatively even distribution of certified nursing assistants, licensed vocational or registered nurses, and nonfrontline staff. Most COVID-19 cases (71%) were detected during mandated weekly NH surveillance testing and most staff (67%) had ≥1 symptom prior to their test. Only 34% of those with symptoms disclosed their symptom to a supervisor. Responses were consistent across 8 discrete survey constructs with Cronbach α > 0.70. In the first wave of the pandemic, fear and lack of knowledge were drivers of symptom reporting. In later waves, adequate staffing and sick days were drivers of symptom reporting. COVID-19 help lines and encouragement from supervisors facilitated symptom reporting and testing. Conclusions: Mandatory COVID-19 testing for NH staff is key to identifying staff COVID-19 cases due to reluctance to speak up about existing symptoms. Active encouragement from supervisors to report symptoms and stay home when ill was a major driver of symptom reporting and resultant infection prevention and worker safety measures.
MeSH term(s)	Humans ; COVID-19/diagnosis ; COVID-19/epidemiology ; COVID-19 Testing ; SARS-CoV-2 ; Reproducibility of Results ; Nursing Homes
Language	English
Publishing date	2023-04-04
Publishing country	United States
Document type	Journal Article
ZDB-ID	639378-0
ISSN	1559-6834 ; 0195-9417 ; 0899-823X
ISSN (online)	1559-6834
ISSN	0195-9417 ; 0899-823X
DOI	10.1017/ice.2023.51
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Article ; Online: Cost-effectiveness of severe acute respiratory coronavirus virus 2 (SARS-CoV-2) testing and isolation strategies in nursing homes.

Bartsch, Sarah M / Weatherwax, Colleen / Martinez, Marie F / Chin, Kevin L / Wasserman, Michael R / Singh, Raveena D / Heneghan, Jessie L / Gussin, Gabrielle M / Scannell, Sheryl A / White, Cameron / Leff, Bruce / Huang, Susan S / Lee, Bruce Y

Infection control and hospital epidemiology

2024 , Page(s) 1–8

Abstract: Objective: Nursing home residents may be particularly vulnerable to coronavirus disease 2019 (COVID-19). Therefore, a question is when and how often nursing homes should test staff for COVID-19 and how this may change as severe acute respiratory ... ...

Abstract	Objective: Nursing home residents may be particularly vulnerable to coronavirus disease 2019 (COVID-19). Therefore, a question is when and how often nursing homes should test staff for COVID-19 and how this may change as severe acute respiratory coronavirus virus 2 (SARS-CoV-2) evolves. Design: We developed an agent-based model representing a typical nursing home, COVID-19 spread, and its health and economic outcomes to determine the clinical and economic value of various screening and isolation strategies and how it may change under various circumstances. Results: Under winter 2023-2024 SARS-CoV-2 omicron variant conditions, symptom-based antigen testing averted 4.5 COVID-19 cases compared to no testing, saving $191 in direct medical costs. Testing implementation costs far outweighed these savings, resulting in net costs of $990 from the Centers for Medicare & Medicaid Services perspective, $1,545 from the third-party payer perspective, and $57,155 from the societal perspective. Testing did not return sufficient positive health effects to make it cost-effective [$50,000 per quality-adjusted life-year (QALY) threshold], but it exceeded this threshold in ≥59% of simulation trials. Testing remained cost-ineffective when routinely testing staff and varying face mask compliance, vaccine efficacy, and booster coverage. However, all antigen testing strategies became cost-effective (≤$31,906 per QALY) or cost saving (saving ≤$18,372) when the severe outcome risk was ≥3 times higher than that of current omicron variants. Conclusions: SARS-CoV-2 testing costs outweighed benefits under winter 2023-2024 conditions; however, testing became cost-effective with increasingly severe clinical outcomes. Cost-effectiveness can change as the epidemic evolves because it depends on clinical severity and other intervention use. Thus, nursing home administrators and policy makers should monitor and evaluate viral virulence and other interventions over time.
Language	English
Publishing date	2024-02-15
Publishing country	United States
Document type	Journal Article
ZDB-ID	639378-0
ISSN	1559-6834 ; 0195-9417 ; 0899-823X
ISSN (online)	1559-6834
ISSN	0195-9417 ; 0899-823X
DOI	10.1017/ice.2024.9
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