Environmental Noise in New York City Long-Term Care Facilities: A Window Into the COVID-19 Pandemic.
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ABSTRACT:
SUBMITTER: Martin JL
PROVIDER: S-EPMC7885630 | biostudies-literature | 2021 May
REPOSITORIES: biostudies-literature
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Environmental Noise in New York City Long-Term Care Facilities: A Window Into the COVID-19 Pandemic.
Martin Jennifer L JL Hernandez Diana D Cadogan Mary P MP Brody Abraham A AA Alessi Cathy A CA Mitchell Michael N MN Song Yeonsu Y Smilowitz Jessica J Vedvyas Alok A Qian Yingzhi Y Zhong Hua H Chodosh Joshua J
Journal of the American Medical Directors Association 20210216 5
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| S-EPMC7814258 | biostudies-literature
Project description:BackgroundThis pilot project implemented admission screening for Candida auris (C. auris) using real-time polymerase chain reaction (rt-PCR) in select high-risk units within health care facilities in New York City.MethodsAn admission screening encounter consisted of collecting 2 swabs, to be tested by rt-PCR, and a data collection form for individuals admitted to ventilator units at 2 nursing homes (NHA and NHB), and the ventilator/pulmonary unit, intensive care unit, and cardiac care unit at a hospital (Hospital C) located in New York City from November 2017 to November 2019.ResultsC. auris colonization was identified in 6.9% (n = 188/2,726) of admissions to participating units. Rates were higher among admissions to NHA and NHB (20.7% and 22.0%, respectively) than Hospital C (3.6%). Within Hospital C, the ventilator/pulmonary unit had a higher rate (5.7%) than the intensive care unit (3.8%) or cardiac care unit (2.5%).DiscussionConsistent with prior research, we found that individuals admitted to ventilator units were at higher risk of C. auris colonization.ConclusionsThis project demonstrates the utility of admission screening using rt-PCR testing to rapidly identify C. auris colonization among admissions to health care facilities so that appropriate transmission-based precautions and control measures can be implemented rapidly to help decrease transmission.
| S-EPMC10902794 | biostudies-literature
Project description:BackgroundThe Texas Department of State Health Services (DSHS) Healthcare Safety Unit (HCSU) conducts remote infection control assessments (tele-ICARs) in long-term care facilities (LTCFs) to evaluate COVID-19 infection prevention and control (IPC) knowledge and practices using a standardized assessment tool. Tele-ICARs are used to gauge different IPC measures specific to SARS-CoV-2 and are either proactive--conducted prior to identified cases--or responsive to an outbreak, which is defined as a new SARS-CoV-2 infection in any staff or any facility-onset infection in a resident. State and local partners use findings from the assessments to aid LTCFs by providing targeted and timely resources and support to mitigate identified gaps.MethodsData from tele-ICARs conducted between March 1 and October 30, 2020 were analyzed to assess major gaps across LTCF types. A major gap was defined as 10% or more of facilities not satisfying a specific IPC measure, excluding missing data. Gaps were also assessed by tele-ICAR type: proactive or responsive. Fisher's exact tests and univariate logistic regression were used to characterize significant associations between major IPC gaps and LTCF or tele-ICAR type.ResultsDSHS conducted tele-ICARs in 438 LTCFs in Texas during 8 months; 191 were nursing homes/skilled nursing facilities (NH/SNFs), 206 were assisted living facilities (ALFs) and 41 were other settings. Of the assessments, 264 were proactive and 174 responsive. Major gaps identified were: (1) 22% did not have a preference for alcohol-based hand sanitizer (ABHS) over soap and water; (2) 18.1% were not aware of the contact time for disinfectants in use; (3) 17.9% had not stopped resident communal dining; (4) 16.8% did not audit hand hygiene and PPE compliance; and (5) 11.8% had not stopped inter-facility group activities and extra-facility field trips. When restricting analyses to proactive tele-ICARs, one additional gap was identified: 11.1% of facilities lacked a dedicated space to care for or cohort residents with confirmed SARS-CoV-2 infection. Significantly more ALFs than NH/SNFs had not suspended resident communal dining (P < .001) nor identified a dedicated space to cohort residents with confirmed SARS-CoV-2 infection (P < .001). Significantly more LTCFs that received a responsive ICAR compared to a proactive ICAR reported a preference for ABHS over soap and water (P = .008) and reported suspending communal dining (P < .001) and group activities (P < .001). Also, significantly more LTCFs that received a responsive ICAR compared to a proactive ICAR had identified a dedicated space to cohort residents with confirmed SARS-CoV-2 (P = .009).ConclusionsIncreased facility education and awareness of federal and state guidelines for group activities and communal dining is warranted in Texas, emphasizing the importance of social distancing for preventing the transmission of SARS-CoV-2 in LTCFs, particularly ALFs. CDC recommendations for ABHS versus hand washing should be emphasized, as well as the importance of monitoring and auditing HCP hand hygiene and PPE compliance. Facilities may benefit from additional education and resources about disinfection, to ensure proper selection of disinfectants and understanding of the contact time required for efficacy. Analysis by tele-ICAR type suggests facilities may benefit from identifying space for dedicated COVID-19 units in advance of an outbreak in their facility. Conducting tele-ICARs in LTCFs enables public health agencies to provide direct and individualized feedback to facilities and identify state-wide opportunities for effective interventions in response to SARS-CoV-2.
| S-EPMC9489257 | biostudies-literature
Project description:Diagnostic difficulties may have led to underestimation of rhinovirus infections in long-term care facilities. Using surveillance data, we found that rhinovirus caused 59% (174/297) of respiratory outbreaks in these facilities during 6 months in 2009. Disease was sometimes severe. Molecular diagnostic testing can differentiate these outbreaks from other infections such as influenza.
| S-EPMC3294989 | biostudies-literature
Project description:ObjectiveThe objective of this research was to explore the lived experiences of long-term care facilities' staff during the COVID-19 pandemic and examine if and how the pandemic played a role in their decision to leave their jobs.DesignQualitative study using thematic analysis of semistructured interviews. Interview transcripts were analysed using coding techniques based in grounded theory.ParticipantsA total of 29 staff with various roles across 21 long-term care facilities in 12 states were interviewed.ResultsThe pandemic influenced the staff's decision to leave their jobs in five different ways, namely: (1) It significantly increased the workload; (2) Created more physical and emotional hazards for staff; (3) Constrained the facilities and their staff financially; (4) Deteriorated morale and job satisfaction among the staff and (5) Increased concerns with upper management's commitment to both general and COVID-19-specific procedures.ConclusionsStaff at long-term care facilities discussed a wide variety of reasons for their decision to quit their jobs during the pandemic. Our findings may inform efforts to reduce the rate of turnover in these facilities.
| S-EPMC9755903 | biostudies-literature
Project description:Rationale Due to the cluster and associated comorbidities in residents of long-term care facilities (LTCFs), COVID-19-associated morbidity and mortality are significantly increased. Multiple therapeutic options, including hydroxychloroquine (HCQ) and azithromycin (AZI), were tried initially to treat moderate to severe COVID-19 and high-risk patients in LTCFs, but they were abandoned due to unfavorable reports. As a less toxic option, we initiated treatment with doxycycline (DOXY) very early in the course of illness. DOXY has antiviral, cardioprotective, immunomodulatory, and anti-inflammatory properties, but the efficacy of early intervention with DOXY in high-risk COVID-19 patients in LTCFs is unknown. Objective The goal of this retrospective study is to describe the clinical outcomes of high-risk COVID-19 patients with moderate to severe symptoms in LTCFs after early intervention with DOXY. Design Case-series analysis Setting LTCFs in New York Participants This observational study examines 89 patients who were diagnosed with COVID-19 from March 18 to May 13, 2020. Exposure All patients who were diagnosed with COVID-19 received DOXY and regular standard of care within 12 hours of the onset of symptoms. Additionally, four patients received meropenem, three patients received Zosyn, two patients received linezolid, and two patients received Bactrim DS. Four patients were on chronic ventilator support. No patients received any steroids or any other antiviral or immunomodulatory agents. The majority of the patients received zinc and calcium supplements as well. Main outcomes and measures Assessed measures were patients' characteristics, fever, shortness of breath (SOB), cough, oxygen saturation/pulse oximetry (POX), radiologic improvements, laboratory tests, DOXY side effects, hospital transfers, and death. Results Eighty-nine (89) high-risk patients, who developed a sudden onset of fever, cough, SOB, and hypoxia and were diagnosed with COVID-19, were treated with DOXY (100 mg PO or intravenous (IV) for seven days) and regular standard of care. Eighty-five percent (85%) of patients (n=76) demonstrated clinical recovery that is defined as resolution of fever (average 3.7 days, Coeff = -0.96, p = 0.0001), resolution of SOB (average 4.2 days), and improvement of POX: average 84% before treatment and average 95% after treatment (84.7 ± 7% vs. 95 ± 2.6%, p = 0.0001). Higher pre- and post-treatment POX is associated with lower mortality (oxygen saturation (Spo2) vs. Death, Coeff = -0.01, p = 0.023; post-Spo2 vs. Death, Coeff = -0.05, p = 0.0002). Within 10 days of symptom onset, 3% of patients (n=3) were transferred to hospital due to clinical deterioration and 11% of patients (n=10) died. The result was followed for 30 days from the onset of symptoms in each patient. Conclusion Early treatment with DOXY for high-risk patients with moderate to severe COVID-19 infections in non-hospital settings, such as LTCFs, is associated with early clinical recovery, decreased hospitalization, and decreased mortality.
| S-EPMC7419149 | biostudies-literature
Project description:BackgroundTuberculous meningitis (TBM) is the most devastating clinical presentation of infection with Mycobacterium tuberculosis; delayed initiation of effective antituberculosis therapy is associated with poor treatment outcomes. Our objective was to determine the relationship between drug resistance and 10-year mortality among patients with TBM.MethodsWe conducted a retrospective cohort study of 324 patients with culture-confirmed TBM, susceptibility results reported for isoniazid and rifampin, and initiation of at least 2 antituberculosis drugs, reported to the tuberculosis registry in New York City between 1 January 1992 and 31 December 2001. Date of death was ascertained by matching the tuberculosis registry with death certificate data for 1992-2012 from the New York Office of Vital Statistics. Human immunodeficiency virus (HIV) status was ascertained by medical records review, matching with the New York City HIV Surveillance registry, and review of cause of death.ResultsAmong 257 TBM patients without rifampin-resistant isolates, isoniazid resistance was associated with mortality after the first 60 days of treatment when controlling for age and HIV infection (adjusted hazard ratio, 1.94 [95% confidence interval, 1.08-3.94]). Death occurred before completion of antituberculosis therapy in 63 of 67 TBM patients (94%) with rifampin-resistant disease.ConclusionsAmong patients with culture-confirmed TBM, we observed rapid early mortality in patients with rifampin-resistant isolates, and an independent association between isoniazid-resistant isolates and death after 60 days of therapy. These findings support the continued evaluation of rapid diagnostic techniques and the empiric addition of second-line drugs for patients with clinically suspected drug-resistant TBM.
| S-EPMC5850536 | biostudies-literature
Project description:BackgroundThis study uses an interpretive description approach to explore nurses' experiences with pressure sore care in long-term care facilities, highlighting the complexity of their encounters.Methods10 experienced nurses from South Korean small to medium-sized hospitals were chosen via purposive sampling for interviews. Data analysis utilized Braun and Clarke's reflexive thematic analysis.ResultsThe study's findings collectively reflect the intricate journey of nurses in Long-term care facilities as they grapple with the challenges of pressure sore care. These nurses transform their perception of pressure sores, encountering a dynamic shift in their understanding. They face multifaceted challenges in providing optimal care, marked by resource constraints and the absence of standardized guidelines. Emotionally, they navigate a complex terrain of sentiments, oscillating between feelings of accomplishment and helplessness. Despite these hurdles, they persistently strive for excellence in pressure sore nursing, driven by an unwavering commitment to delivering the best possible care within the confines of their healthcare environment.ConclusionThe study enhances understanding of pressure sore care in long-term facilities and suggests avenues for improving nursing practices and care quality through targeted interventions.
| S-EPMC10911102 | biostudies-literature
Project description:BackgroundLong-term care facilities (LTCFs) are vulnerable to disease outbreaks. Here, we jointly analyze SARS-CoV-2 genomic and paired epidemiologic data from LTCFs and surrounding communities in Washington state (WA) to assess transmission patterns during 2020-2022, in a setting of changing policy. We describe sequencing efforts and genomic epidemiologic findings across LTCFs and perform in-depth analysis in a single county.MethodsWe assessed genomic data representativeness, built phylogenetic trees, and conducted discrete trait analysis to estimate introduction sizes over time, and explored selected outbreaks to further characterize transmission events.ResultsWe found that transmission dynamics among cases associated with LTCFs in WA changed over the course of the COVID-19 pandemic, with variable introduction rates into LTCFs, but decreasing amplification within LTCFs. SARS-CoV-2 lineages circulating in LTCFs were similar to those circulating in communities at the same time. Transmission between staff and residents was bi-directional.ConclusionsUnderstanding transmission dynamics within and between LTCFs using genomic epidemiology on a broad scale can assist in targeting policies and prevention efforts. Tracking facility-level outbreaks can help differentiate intra-facility outbreaks from high community transmission with repeated introduction events. Based on our study findings, methods for routine tree building and overlay of epidemiologic data for hypothesis generation by public health practitioners are recommended. Discrete trait analysis added valuable insight and can be considered when representative sequencing is performed. Cluster detection tools, especially those that rely on distance thresholds, may be of more limited use given current data capture and timeliness. Importantly, we noted a decrease in data capture from LTCFs over time. Depending on goals for use of genomic data, sentinel surveillance should be increased or targeted surveillance implemented to ensure available data for analysis.
| S-EPMC10789038 | biostudies-literature
Project description:BackgroundShort-term fall prediction models that use electronic health records (EHRs) may enable the implementation of dynamic care practices that specifically address changes in individualized fall risk within senior care facilities.ObjectiveThe aim of this study is to implement machine learning (ML) algorithms that use EHR data to predict a 3-month fall risk in residents from a variety of senior care facilities providing different levels of care.MethodsThis retrospective study obtained EHR data (2007-2021) from Juniper Communities' proprietary database of 2785 individuals primarily residing in skilled nursing facilities, independent living facilities, and assisted living facilities across the United States. We assessed the performance of 3 ML-based fall prediction models and the Juniper Communities' fall risk assessment. Additional analyses were conducted to examine how changes in the input features, training data sets, and prediction windows affected the performance of these models.ResultsThe Extreme Gradient Boosting model exhibited the highest performance, with an area under the receiver operating characteristic curve of 0.846 (95% CI 0.794-0.894), specificity of 0.848, diagnostic odds ratio of 13.40, and sensitivity of 0.706, while achieving the best trade-off in balancing true positive and negative rates. The number of active medications was the most significant feature associated with fall risk, followed by a resident's number of active diseases and several variables associated with vital signs, including diastolic blood pressure and changes in weight and respiratory rates. The combination of vital signs with traditional risk factors as input features achieved higher prediction accuracy than using either group of features alone.ConclusionsThis study shows that the Extreme Gradient Boosting technique can use a large number of features from EHR data to make short-term fall predictions with a better performance than that of conventional fall risk assessments and other ML models. The integration of routinely collected EHR data, particularly vital signs, into fall prediction models may generate more accurate fall risk surveillance than models without vital signs. Our data support the use of ML models for dynamic, cost-effective, and automated fall predictions in different types of senior care facilities.
| S-EPMC9015781 | biostudies-literature