Stay-at-home policy is a case of exception fallacy: an internet-based ecological study.
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ABSTRACT:
SUBMITTER: Savaris RF
PROVIDER: S-EPMC7935901 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundGlobally, nonpharmaceutical interventions for COVID-19, including stay-at-home policies, limitations on gatherings and closure of public spaces, are being lifted. We explored the effect of lifting a stay-at-home policy on virus resurgence under different conditions.MethodsUsing confirmed case data from Toronto, Canada, between Feb. 24 and June 24, 2020, we ran a compartmental model with household structure to simulate the impact of the stay-at-home policy considering different levels of compliance. We estimated threshold values for the maximum number of contacts, probability of transmission and testing rates required for the safe reopening of the community.ResultsAfter the implementation of the stay-at-home policy, the contact rate outside the household fell by 39% (from 11.58 daily contacts to 7.11). The effective reproductive number decreased from 3.56 (95% confidence interval [CI] 3.02-4.14) on Mar. 12 to 0.84 (95% CI 0.79-0.89) on May 6. Strong adherence to stay-at-home policies appeared to prevent SARS-CoV-2 resurgence, but extending the duration of stay-at-home policies beyond 2 months had little added effect on cumulative cases (25 958 for 65 days of a stay-at-home policy and 23 461 for 95 days, by July 2, 2020) and deaths (1404 for 65 days and 1353 for 95 days). To avoid a resurgence, the average number of contacts per person per day should be kept below 9, with strict nonpharmaceutical interventions in place.InterpretationOur study demonstrates that the stay-at-home policy implemented in Toronto in March 2020 had a substantial impact on mitigating the spread of SARS-CoV-2. In the context of the early pandemic, before the emergence of variants of concern, reopening schools and workplaces was possible only with other nonpharmaceutical interventions in place.
| S-EPMC9022937 | biostudies-literature
Project description:BackgroundDue to the increasingly ageing society and the shortage of nursing human resources in China, the imbalance between the home care needs of older patients and the inadequate supply of nursing services is increasing. Based on this medical situation, China is implementing internet-based home care (with the nurses who provide this care called online nurses or sharing nurses) based on the concept of the sharing economy, internet technology and knowledge from the home care experience in other countries. Internet-based home care follows an online application/offline service model. Patients place orders through an app, nurses grab orders instantly, and managers dispatch orders through a web platform based on various factors such as nurses' qualifications, professionalism and distance from the patient. In this way, home care is provided for patients with limited mobility, such as older or disabled patients, patients in rehabilitation and terminal patients. Only by fully understanding the barriers to accessing internet-based home care can we provide quality nursing services to older patients and achieve the sustainable development of internet-based home care.ObjectiveThe goal of this study was to use qualitative methods to explore barriers to accessing internet-based home care for older patients.MethodsBased on Levesque's access to health care framework, semi-structured personal interviews were conducted with 19 older patients in a descriptive qualitative study using directed content analysis.ResultsWe identified four barriers to accessing internet-based home care for older patients: barriers to perceiving, seeking, paying for, and engaging in internet-based home care. Specific barriers included traditional perceptions, barriers to internet use, high payment costs, uneven quality of services, and concerns about privacy and patient safety.ConclusionsInternet-based home care brings new risks and challenges. In order to enable older patients to better enjoy it, it is necessary to strengthen publicity, optimize the network application process, improve the health insurance system, formulate unified nursing service standards, and address safety risks.
| S-EPMC8522081 | biostudies-literature
Project description:Background: Early mitigation orders for COVID-19 halted participation in community-based programs. We examined the early impact of "Stay Home, Stay Healthy" orders on functioning in older adults participating in a behavioral intervention study involving community-based exercise. Methods: A quasi-natural experiment, using mixed methods (n = 39). Participants completed interviews and questionnaires after 3-4 weeks of the Stay Home, Stay Healthy directive. PROMIS-29 outcomes were compared to pre-COVID-19 responses. Results: Participants had a mean age of 74.1 (6.5) years, 79.5% were women, and 20.5% were racial/ethnic minorities. Compared to pre-COVID-19, there was a significant increase in anxiety and decrease in fatigue and social participation. Thematic analysis revealed five main themes related to disruption of daily life, the emotional and physical impact of stay-at-home orders, unexpected positive outcomes, and perspectives on messaging surrounding the pandemic. Conclusions: Efforts to curb the spread of COVID-19 have substantially impacted the lives of older adults participating in community-based exercise.
| S-EPMC8238828 | biostudies-literature
Project description:BackgroundHome visits are an important part of home care. With increasing demand and the rapid development of information technology, an increasing number of regions are experimenting with the use of information technology in home visits, hoping to meet the needs of more patients through technological interventions. However, most of the current studies have focused on patient health improvement through home visits, neglecting to consider the actual experience of nurses as service providers in participating in Internet-based programs. Thus, the purpose of this research is to explore what is holding nurses back from participating after the Internet has been added to traditional home visiting programs.MethodsThis research was designed with an exploratory-descriptive qualitative analysis method. Semistructured interviews were used to collect information on barriers to nurses' participation in the Internet-based home visiting program. Participants included 16 clinical nurses working in various hospitals in Nanjing, China. The thematic analysis method was used to analyze the information.ResultsThis research identified three themes and twelve subthemes that hinder clinical nurse engagement in the Internet-based home visiting program. The three themes included multiple barriers to individuals, different service modes, and emerging organizational problems.ConclusionsAs a new form of traditional home visiting program in information society, Internet-based home visiting has many shortcomings in the overall program design and service management specifications. For more patients living at home to receive quality care services, it is necessary to take more effective measures to encourage nurses' participation at three levels: nurse demand, service process, and organizational management.
| S-EPMC10729343 | biostudies-literature
Project description:During the COVID-19 pandemic, many states imposed stay-at-home (SAH) and mandatory face mask (MFM) orders to supplement the United States CDC recommendations. The purpose of this study was to characterize the relationship between SAH and MFM approaches with the incidence and fatality of COVID-19 during the pandemic period until 23 August 2020 (about 171 days), the period with no vaccines or specific drugs that had passed the phase III clinical trials yet. States with SAH orders showed a potential 50-60% decrease in infection and fatality during the SAH period (about 45 days). After normalization to population density, there was a 44% significant increase in the fatality rate in no-SAH + no-MFM states when compared to SAH + MFM. However, many results in this study were inconsistent with the intent of public health strategies of SAH and MFM. There were similar incidence rates (1.41, 1.81, and 1.36%) and significant differences in fatality rates (3.40, 2.12, and 1.25%; p < 0.05) and mortality rates (51.43, 34.50, and 17.42 per 100,000 residents; p < 0.05) among SAH + MFM, SAH + no-MFM, and no-SAH + no-MFM states, respectively. There were no significant differences in total positive cases, average daily new cases, and average daily fatality when normalized with population density among the three groups. This study suggested potential decreases in infection and fatality with short-term SAH order. However, SAH and MFM orders from some states' policies probably had limited effects in lowering transmission and fatality among the general population. At the policy-making level, if contagious patients would not likely be placed in strict isolation and massive contact tracing would not be effective to implement, we presume that following the CDC's recommendations with close monitoring of healthcare capacity could be appropriate in helping mitigate the COVID-19 disaster while limiting collateral socioeconomic damages.
| S-EPMC9609417 | biostudies-literature
Project description:Resident and bacterial genomic features associated with clinical Enterobacterales isolates from care homes
| PRJEB80736 | ENA
Project description:Prevalence of multidrug resistant organisms (MDRO) in nursing homes in Ireland and the Netherlands
| PRJEB31196 | ENA
Project description:BACKGROUND:Traditional methods using print media and commercial firms for clinician recruiting are often limited by cost, slow pace, and suboptimal results. An efficient and fiscally sound approach is needed for searching online to recruit clinicians. OBJECTIVE:The aim of the study was to assess the Web-based methods by which clinicians might be searching for jobs in a broad range of specialties and how academic medical centers can advertise clinical job openings to prominently appear on internet searches that would yield the greatest return on investment. METHODS:We used a search engine (Google) to identify 8 query terms for each of the specialties and specialists (eg, dermatology and dermatologist) to determine internet job search methodologies for 12 clinical disciplines. Searches were conducted, and the data used for analysis were the first 20 results. RESULTS:In total, 176 searches were conducted at varying times over the course of several months, and 3520 results were recorded. The following 4 types of websites appeared in the top 10 search results across all specialties searched, accounting for 52.27% (920/1760) of the results: (1) a single no-cost job aggregator (229/1760, 13.01%); (2) 2 prominent journal-based paid digital job listing services (157/1760, 8.92% and 91/1760, 5.17%, respectively); (3) a fee-based Web-based agency (137/1760, 7.78%) offering candidate profiles; and (4) society-based paid advertisements (totaling 306/1760, 17.38%). These sites accounted for 75.45% (664/880) of results limited to the top 5 results. Repetitive short-term testing yielded similar results with minor changes in the rank order. CONCLUSIONS:On the basis of our findings, we offer a specific financially prudent internet strategy for both clinicians searching the internet for employment and employers hiring clinicians in academic medical centers.
| S-EPMC6640069 | biostudies-other
Project description:Background: Recently, internet hospitals have been emerging in China, saving patients time and money during the COVID-19 pandemic. In addition, pharmacy services that link doctors and patients are becoming essential in improving patient satisfaction. However, the existing internet hospital pharmacy service mode relies primarily on manual operations, making it cumbersome, inefficient, and high-risk. Objective: To establish an internet hospital pharmacy service mode based on artificial intelligence (AI) and provide new insights into pharmacy services in internet hospitals during the COVID-19 pandemic. Methods: An AI-based internet hospital pharmacy service mode was established. Initially, prescription rules were formulated and embedded into the internet hospital system to review the prescriptions using AI. Then, the "medicine pick-up code," which is a Quick Response (QR) code that represents a specific offline self-pick-up order, was created. Patients or volunteers could pick up medications at an offline hospital or drugstore by scanning the QR code through the window and wait for the dispensing machine or pharmacist to dispense the drugs. Moreover, the medication consultation function was also operational. Results: The established internet pharmacy service mode had four major functional segments: online drug catalog search, prescription preview by AI, drug dispensing and distribution, and AI-based medication consultation response. The qualified rate of AI preview was 83.65%. Among the 16.35% inappropriate prescriptions, 49% were accepted and modified by physicians proactively and 51.00% were passed after pharmacists intervened. The "offline self-pick-up" mode was preferred by 86% of the patients for collecting their medication in the internet hospital, which made the QR code to be fully applied. A total of 426 medication consultants were served, and 48.83% of them consulted outside working hours. The most frequently asked questions during consultations were about the internet hospital dispensing process, followed by disease diagnosis, and patient education. Therefore, an AI-based medication consultation was proposed to respond immediately when pharmacists were unavailable. Conclusion: The established AI-based internet hospital pharmacy service mode could provide references for pharmacy departments during the COVID-19 pandemic. The significance of this study lies in ensuring safe/rational use of medicines and raising pharmacists' working efficiency.
| S-EPMC9682042 | biostudies-literature
Project description:Effective disease surveillance is critical to the functioning of health systems. Traditional approaches are, however, limited in their ability to deliver timely information. Internet-based surveillance systems are a promising approach that may circumvent many of the limitations of traditional health surveillance systems and provide more intelligence on cases of infection, including cases from those that do not use the healthcare system. Infectious disease surveillance systems built on Internet search metrics have been shown to produce accurate estimates of disease weeks before traditional systems and are an economically attractive approach to surveillance; they are, however, also prone to error under certain circumstances. This study sought to explore previously unmodeled diseases by investigating the link between Google Trends search metrics and Australian weekly notification data. We propose using four alternative disease modelling strategies based on linear models that studied the length of the training period used for model construction, determined the most appropriate lag for search metrics, used wavelet transformation for denoising data and enabled the identification of key search queries for each disease. Out of the twenty-four diseases assessed with Australian data, our nowcasting results highlighted promise for two diseases of international concern, Ross River virus and pneumococcal disease.
| S-EPMC5172376 | biostudies-other