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1.
Front Public Health ; 11: 1142299, 2023.
Article in English | MEDLINE | ID: covidwho-2320912

ABSTRACT

Background: The estimated lifetime risk of stroke was the highest in East Asia worldwide, especially in China. Antihypertensive therapy can significantly reduce stroke mortality. However, blood pressure control is poor. Medication adherence is a barrier as patients' out-of-pocket costs have risen. We aimed to take advantage of a free hypertension pharmacy intervention and quantified the impact on stroke mortality. Methods: A free pharmaceutical intervention program was implemented in Deqing, Zhejiang province in April 2018. Another non-pharmaceutical intervention, social distancing due to the pandemic of Coronavirus disease 2019 (COVID-19), was also key to affecting stroke mortality. We retrospectively collected the routine surveillance data of stroke deaths from Huzhou Municipal Center for Disease Prevention and Control in 2013-2020 and obtained within-city mobility data from Baidu Migration in 2019-2020, then we quantified the effects of both pharmaceutical intervention and social distancing using Serfling regression model. Results: Compared to the predicted number, the actual number of stroke deaths was significantly lower by 10% (95% CI, 6-15%; p < 0.001) from April 2018 to December 2020 in Deqing. Specifically, there was a reduction of 19% (95% CI, 10-28%; p < 0.001) in 2018. Moreover, we observed a 5% (95% CI, -4 - 14%; p = 0.28) increase in stroke mortality due to the adverse effect of COVID-19 but it wasn't statistically significant. Conclusion: Free hypertension pharmacy program has great potential to prevent considerable stroke deaths. In the future, the free supply of low-cost, essential medications that target patients with hypertension at increased risk of stroke could be taken into account in formulating public health policies and guiding allocations of health care resources.


Subject(s)
COVID-19 , Hypertension , Pharmacy , Stroke , Humans , Longitudinal Studies , COVID-19/epidemiology , Physical Distancing , Retrospective Studies , Hypertension/drug therapy , Hypertension/epidemiology , Stroke/prevention & control , Policy
2.
BMJ Open ; 12(3): e055880, 2022 03 24.
Article in English | MEDLINE | ID: covidwho-1765125

ABSTRACT

IntroductionSilent cerebrovascular disease (SCD), which is a common disease in the elderly, leads to cognitive decline, gait disorders, depression and urination dysfunction, and increases the risk of cerebrovascular events. Our study aims to compare the accuracy of the diagnosis of SCD-related gait disorders between the intelligent system and the clinician. Our team have developed an intelligent evaluation system for gait. This study will evaluate whether the intelligent system can help doctors make clinical decisions and predictions, which aids the early prevention and treatment of SCD. METHODS AND ANALYSIS: This study is a multi-centred, prospective, randomised and controlled trial.SCD subjects aged 60-85 years in Shanghai and Guizhou will be recruited continuously. All subjects will randomly be divided into a doctor with intelligence assistance group or a doctor group, at a 1:1 ratio. The doctor and intelligent assistant group will accept the intelligent system evaluation. The intelligent system obtains gait parameters by an Red-Green-Blue-depth camera and computer vision algorithm. The doctor group will accept the clinicians' routine treatment procedures. Meanwhile, all subjects will accept the panel's gait assessment and recognition rating scale as the gold standard. The primary outcome is the sensitivity of the intelligent system and clinicians to screen for gait disorders. The secondary outcomes include the healthcare costs and the incremental cost effectiveness ratio of intelligent systems and clinicians to screen for gait disorders. ETHICS AND DISSEMINATION: Approval was granted by the Ethics Committee of Zhongshan Hospital affiliated with Fudan University on 26 November 2019. The approval number is B2019-027(2) R. All subjects will sign an informed consent form before enrolment. Serious adverse events will be reported to the main researchers and ethics committees. The subjects' data will be kept strictly confidential. The results will be disseminated in peer-reviewed journals. TRIAL REGISTRATION NUMBER: NCT04457908.


Subject(s)
Cerebrovascular Disorders , Gait , Aged , Cerebrovascular Disorders/diagnosis , China , Cost-Benefit Analysis , Humans , Multicenter Studies as Topic , Prospective Studies , Randomized Controlled Trials as Topic
3.
BMC Infect Dis ; 20(1): 959, 2020 Dec 17.
Article in English | MEDLINE | ID: covidwho-979676

ABSTRACT

BACKGROUND: Previous published prognostic models for COVID-19 patients have been suggested to be prone to bias due to unrepresentativeness of patient population, lack of external validation, inappropriate statistical analyses, or poor reporting. A high-quality and easy-to-use prognostic model to predict in-hospital mortality for COVID-19 patients could support physicians to make better clinical decisions. METHODS: Fine-Gray models were used to derive a prognostic model to predict in-hospital mortality (treating discharged alive from hospital as the competing event) in COVID-19 patients using two retrospective cohorts (n = 1008) in Wuhan, China from January 1 to February 10, 2020. The proposed model was internally evaluated by bootstrap approach and externally evaluated in an external cohort (n = 1031). RESULTS: The derivation cohort was a case-mix of mild-to-severe hospitalized COVID-19 patients (43.6% females, median age 55). The final model (PLANS), including five predictor variables of platelet count, lymphocyte count, age, neutrophil count, and sex, had an excellent predictive performance (optimism-adjusted C-index: 0.85, 95% CI: 0.83 to 0.87; averaged calibration slope: 0.95, 95% CI: 0.82 to 1.08). Internal validation showed little overfitting. External validation using an independent cohort (47.8% female, median age 63) demonstrated excellent predictive performance (C-index: 0.87, 95% CI: 0.85 to 0.89; calibration slope: 1.02, 95% CI: 0.92 to 1.12). The averaged predicted cumulative incidence curves were close to the observed cumulative incidence curves in patients with different risk profiles. CONCLUSIONS: The PLANS model based on five routinely collected predictors would assist clinicians in better triaging patients and allocating healthcare resources to reduce COVID-19 fatality.


Subject(s)
COVID-19/mortality , Models, Statistical , Adult , Aged , COVID-19/blood , COVID-19/pathology , China/epidemiology , Female , Hospital Mortality , Hospitalization , Humans , Leukocyte Count , Lymphocytes/pathology , Male , Middle Aged , Neutrophils/pathology , Platelet Count , Prognosis , Reproducibility of Results , Retrospective Studies , SARS-CoV-2
4.
Front Public Health ; 8: 205, 2020.
Article in English | MEDLINE | ID: covidwho-854039

ABSTRACT

The COVID-19 outbreak spread rapidly throughout the globe, with worldwide infections and deaths continuing to increase dramatically. To control disease spread and protect healthcare workers, accurate information is necessary. We searched PubMed and Google Scholar for studies published from December 2019 to March 31, 2020 with the terms "COVID-19," "2019-nCoV," "SARS-CoV-2," or "Novel Coronavirus Pneumonia." The main symptoms of COVID-19 are fever (83-98.6%), cough (59.4-82%), and fatigue (38.1-69.6%). However, only 43.8% of patients have fever early in the disease course, despite still being infectious. These patients may present to clinics lacking proper precautions, leading to nosocomial transmission, and infection of workers. Potential COVID-19 cases must be identified early to initiate proper triage and distinguish them quickly from similar infections. Early identification, accurate triage, and standardized personal protection protocols can reduce the risk of cross infection. Containing disease spread will require protecting healthcare workers.


Subject(s)
COVID-19 , Cough/etiology , Fever/etiology , Health Personnel/statistics & numerical data , COVID-19/diagnosis , COVID-19/transmission , Global Health , Humans , Infection Control , Risk Assessment , SARS-CoV-2
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