Reducing hospital admissions for COVID-19 at a dedicated screening centre in Singapore.
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ABSTRACT:
SUBMITTER: Tan GSE
PROVIDER: S-EPMC7228894 | biostudies-literature | 2020 Sep
REPOSITORIES: biostudies-literature
Publications
Reducing hospital admissions for COVID-19 at a dedicated screening centre in Singapore.
Tan G S E GSE Ang H H Manauis C M CM Chua J M JM Gao C Q CQ Ng F K K FKK Wong C S CS Ng O T OT Marimuthu K K Chan M M Leo Y-S YS Vasoo S S
Clinical microbiology and infection : the official publication of the European Society of Clinical Microbiology and Infectious Diseases 20200516 9
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Project description:Background There is a need to establish the effectiveness of the coronavirus disease 2019 (COVID-19) vaccines in reducing COVID-19-related hopitalization of patients in Jordan. As the vaccination program accelerates, it is important to determine whether the vaccines' effectiveness (VE) has successfully reduced the number of acute cases admitted to hospital. Methods To determine the efficacy of Pfizer-BioNTech and Sinopharm COVID-19 vaccines among Jordanian patients admitted to Prince Hamza hospital, a single center case-control study was performed. The study analyzed the hospitalization rates of vaccinated (n = 536) and unvaccinated (n = 585) individuals across the 2-month period from February 6 to April 6, 2022. The cases were patients who tested positive for SARS-CoV-2 (“case-patients”), whilst the control group were hospital patients who did not test positive for SARS-CoV-2 (“control-patients”). Results This study found that among 1,121 total participants (561 cases and 560 control), the overall vaccine effectiveness (VE) among the participants was 84% (95% Cl 79–88%). VE was higher in females (88%, 95% Cl 84–93%) than in males (77%, 95% Cl 67–84%) (p < 0.001), and it was highest in those between the ages of 18 and 28-years-old (95%, 95% CI 86–98%). For patients with pre-existing conditions, including chronic heart disease, chronic lung disease, and diabetes, VE was higher compared to patients with no comorbidities, though the difference was not statistically significant. Finally, in comparing all vaccinated participants, VE was higher for those who received the Pfizer vaccine (VE = 92%, 95% CI 88–94%) (OR 0.08, 95% CI 0.06–0.12) than for those who received the Sinopharm vaccine (VE = 67%, 95% CI 52–78%) (OR 0.33, 95% CI 0.22–0.48); (p = 0.011). Conclusion Overall, Pfizer and Sinopharm vaccines were found to be effective in limiting hospitalizations for acute cases of coronavirus among Jordanian adult's patient's cohort between February 6 and April 6, 2022, especially among patients with comorbidities.
| S-EPMC9532618 | biostudies-literature
Project description:ObjectiveTo explore the impact of the COVID-19 on the distribution, type and patterns of diseases in hospitalised children under local antiepidemic measures.DesignRetrospective chart review.SettingElectronic medical records of patients hospitalised in the paediatric department of a tertiary hospital in South China from 21 January 2019 to 20 January 2021.ParticipantsRecords of 2139 patients.Outcome measuresData were analysed before and during the COVID-19 pandemic. Disease characteristics were analysed based on the 10th revision of the International Statistical Classification of Diseases and Related Health Problems. Features of the length of hospital stay were investigated. Categorical variables involving more than three groups were analysed using an overall χ2 test, followed by pairwise comparisons.ResultsDuring the COVID-19 outbreak period, paediatric hospitalisation was reduced by 29.6%, from 1255 to 884. The proportions of infection-related diseases (36.3% (455 cases) vs 20.8% (184 cases)), respiratory system-related diseases (22.5% (283 cases) vs 9.4% (83 cases)); and endocrine, nutritional and metabolic diseases (17.1% (214 cases) vs 9.2% (81 cases)) decreased significantly, whereas that of musculoskeletal and connective tissue diseases increased from 11.0% (138 cases) to 20.1% (178 cases), thereby becoming the most common reason for hospitalisation. The proportions of diseases of the nervous system (12.4% (156 cases) to 18.8% (166 cases)) and mental and behavioural disorders (0.2% (3 cases) to 2.1% (19 cases)) increased significantly. The average length of hospital stay increased after the outbreak (7.57±6.53 vs 8.36±6.87).ConclusionThe number of hospitalisation cases decreased during the COVID-19 period. The prominent decreases in hospitalisation associated with infections and respiratory system diseases were likely attributed to the improved epidemic prevention work, enhancement of people's health awareness and fear of possible exposure to COVID-19. Describing the impact of COVID-19 on disease patterns may provide a reference for resource planning during the pandemic.
| S-EPMC9254659 | biostudies-literature
Project description:BackgroundA randomized controlled trial involving a high-risk, unvaccinated population that was conducted before the Omicron variant emerged found that nirmatrelvir-ritonavir was effective in preventing progression to severe COVID-19. Our objective was to evaluate the effectiveness of nirmatrelvir-ritonavir in preventing severe COVID-19 while Omicron and its subvariants predominate.MethodsWe conducted a population-based cohort study in Ontario that included all residents who were older than 17 years of age and had a positive polymerase chain reaction test for SARS-CoV-2 between Apr. 4 and Aug. 31, 2022. We compared patients treated with nirmatrelvir-ritonavir with patients who were not treated and measured the primary outcome of hospital admission from COVID-19 or all-cause death at 1-30 days, and a secondary outcome of all-cause death. We used weighted logistic regression to calculate weighted odds ratios (ORs) with confidence intervals (CIs) using inverse probability of treatment weighting (IPTW) to control for confounding.ResultsThe final cohort included 177 545 patients, 8876 (5.0%) who were treated with nirmatrelvir-ritonavir and 168 669 (95.0%) who were not treated. The groups were well balanced with respect to demographic and clinical characteristics after applying stabilized IPTW. We found that the occurrence of hospital admission or death was lower in the group given nirmatrelvir-ritonavir than in those who were not (2.1% v. 3.7%; weighted OR 0.56, 95% CI 0.47-0.67). For death alone, the weighted OR was 0.49 (95% CI 0.39-0.62). Our findings were similar across strata of age, drug-drug interactions, vaccination status and comorbidities. The number needed to treat to prevent 1 case of severe COVID-19 was 62 (95% CI 43-80), which varied across strata.InterpretationNirmatrelvir-ritonavir was associated with significantly reduced odds of hospital admission and death from COVID-19, which supports use to treat patients with mild COVID-19 who are at risk for severe disease.
| S-EPMC9928441 | biostudies-literature
Project description:BACKGROUND: The geriatric nursing home population is vulnerable to acute and deteriorating illness due to advanced age, multiple chronic illnesses and high levels of dependency. Although the detriments of hospitalising the frail and old are widely recognised, hospital admissions from nursing homes remain common. Little is known about what alternatives exist to prevent and reduce hospital admissions from this setting. The objective of this study, therefore, is to summarise the effects of interventions to reduce acute hospitalisations from nursing homes. METHODS: A systematic literature search was performed in Cochrane Library, PubMed, MEDLINE, EMBASE and ISI Web of Science in April 2013. Studies were eligible if they had a geriatric nursing home study population and were evaluating any type of intervention aiming at reducing acute hospital admission. Systematic reviews, randomised controlled trials, quasi randomised controlled trials, controlled before-after studies and interrupted time series were eligible study designs. The process of selecting studies, assessing them, extracting data and grading the total evidence was done by two researchers individually, with any disagreement solved by a third. We made use of meta-analyses from included systematic reviews, the remaining synthesis is descriptive. Based on the type of intervention, the included studies were categorised in: 1) Interventions to structure and standardise clinical practice, 2) Geriatric specialist services and 3) Influenza vaccination. RESULTS: Five systematic reviews and five primary studies were included, evaluating a total of 11 different interventions. Fewer hospital admissions were found in four out of seven evaluations of structuring and standardising clinical practice; in both evaluations of geriatric specialist services, and in influenza vaccination of residents. The quality of the evidence for all comparisons was of low or very low quality, using the GRADE approach. CONCLUSIONS: Overall, eleven interventions to reduce hospital admissions from nursing homes were identified. None of them were tested more than once and the quality of the evidence was low for every comparison. Still, several interventions had effects on reducing hospital admissions and may represent important aspects of nursing home care to reduce hospital admissions.
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Project description:Background: The COVID-19 pandemic disrupted hospital care, as hospitals had to deal with a highly infectious virus, while at the same time continuing to fulfill the ongoing health service needs of their communities. This study examines the direct effects of COVID-19 on the delivery of inpatient care in Croatia. Materials and Methods: The research is a retrospective, comparative analysis of the hospital admission rate across all Diagnosis Related Group (DRG) classes before and during the pandemic. It is based on DRG data from all non-specialized acute hospitals in Croatia, which account for 96% of national inpatient activity. The study also used COVID-19 data from the Croatian Institute of Public Health (CIPH). Results: The results show a 21% decrease in the total number of admissions [incident rate ratio (IRR) 0.8, p < 0.0001] across the hospital network during the pandemic in 2020, with the greatest drop occurring in April, when admissions plunged by 51%. The decrease in activity occurred in non-elective DRG classes such as cancers, stroke, major chest procedures, heart failure, and renal failure. Coinciding with this reduction however, there was a 37% increase (IRR 1.39, p < 0.0001) in case activity across six COVID-19 related DRG classes. Conclusions: The reduction in hospital inpatient activity during 2020, can be attributed to a number of factors such as lock-downs and quarantining, reorganization of hospital operations, the rationing of the medical workforce, and the reluctance of people to seek hospital care. Further research is needed to examine the consequences of disruption to hospital care in Croatia. Our recommendation is to invest multidisciplinary effort in reviewing response procedures to emergencies such as COVID-19 with the aim of minimizing their impact on other, and equally important community health care needs.
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Project description:BackgroundFor each of the COVID-19 pandemic waves, hospitals have had to plan for deploying surge capacity and resources to manage large but transient increases in COVID-19 admissions. While a lot of effort has gone into predicting regional trends in COVID-19 cases and hospitalizations, there are far fewer successful tools for creating accurate hospital-level forecasts.MethodsLarge-scale, anonymized mobile phone data has been shown to correlate with regional case counts during the first two waves of the pandemic (spring 2020, and fall/winter 2021). Building off this success, we developed a multi-step, recursive forecasting model to predict individual hospital admissions; this model incorporates the following data: (i) hospital-level COVID-19 admissions, (ii) statewide test positivity data, and (iii) aggregate measures of large-scale human mobility, contact patterns, and commuting volume.ResultsIncorporating large-scale, aggregate mobility data as exogenous variables in prediction models allows us to make hospital-specific COVID-19 admission forecasts 21 days ahead. We show this through highly accurate predictions of hospital admissions for five hospitals in Massachusetts during the first year of the COVID-19 pandemic.ConclusionsThe high predictive capability of the model was achieved by combining anonymized, aggregated mobile device data about users' contact patterns, commuting volume, and mobility range with COVID hospitalizations and test-positivity data. Mobility-informed forecasting models can increase the lead-time of accurate predictions for individual hospitals, giving managers valuable time to strategize how best to allocate resources to manage forthcoming surges.
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Project description:The transmission of COVID-19 is dependent on social mixing, the basic rate of which varies with sociodemographic, cultural, and geographic factors. Alterations in social mixing and subsequent changes in transmission dynamics eventually affect hospital admissions. We employ these observations to model and predict regional hospital admissions in Sweden during the COVID-19 pandemic. We use an SEIR-model for each region in Sweden in which the social mixing is assumed to depend on mobility data from public transport utilisation and locations for mobile phone usage. The results show that the model could capture the timing of the first and beginning of the second wave of the pandemic 3 weeks in advance without any additional assumptions about seasonality. Further, we show that for two major regions of Sweden, models with public transport data outperform models using mobile phone usage. We conclude that a model based on routinely collected mobility data makes it possible to predict future hospital admissions for COVID-19 3 weeks in advance.
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