COVI-Prim international: Similarities and discrepancies in the way general practices from seven different countries coped with the COVID-19 pandemic.

Objectives: General practitioners (GPs) are frequently patients' first point of contact with the healthcare system and play an important role in identifying, managing and monitoring cases. This study investigated the experiences of GPs from seven different countries in the early phases of the COVID-19 pandemic. Design: International cross-sectional online survey. Setting: General practitioners from Australia, Austria, Germany, Hungary, Italy, Slovenia and Switzerland. Participants: Overall, 1,642 GPs completed the survey. Main outcome measures: We focused on how well-prepared GPs were, their self-confidence and concerns, efforts to control the spread of the disease, patient contacts, information flow, testing procedures and protection of staff. Results: GPs gave high ratings to their self-confidence (7.3, 95% CI 7.1-7.5) and their efforts to control the spread of the disease (7.2, 95% CI 7.0-7.3). A decrease in the number of patient contacts (5.7, 95% CI 5.4-5.9), the perception of risk (5.3 95% CI 4.9-5.6), the provision of information to GPs (4.9, 95% CI 4.6-5.2), their testing of suspected cases (3.7, 95% CI 3.4-3.9) and their preparedness to face a pandemic (mean: 3.5; 95% CI 3.2-3.7) were rated as moderate. GPs gave low ratings to their ability to protect staff (2.2 95% CI 1.9-2.4). Differences were identified in all dimensions except protection of staff, which was consistently low in all surveyed GPs and countries. Conclusion: Although GPs in the different countries were confronted with the same pandemic, its impact on specific aspects differed. This partly reflected differences in health care systems and experience of recent pandemics. However, it also showed that the development of structured care plans in case of future infectious diseases requires the early involvement of primary care representatives.

Predictors for adherent behavior in the COVID-19 pandemic: A cross-sectional telephone survey.

Background: During the COVID-19 pandemic, protective measures have been prescribed to prevent or slow down the spread of the SARS-CoV-2 virus and protect the population. Individuals follow these measures to varying degrees. We aimed to identify factors influencing the extent to which protective measures are adhered to. Methods: A cross-sectional survey (telephone interviews) was undertaken between April and June 2021 to identify factors influencing the degree to which individuals adhere to protective measures. A representative sample of 1,003 people (age >16 years) in two Austrian states (Carinthia, Vorarlberg) was interviewed. The questionnaire was based on the Health Belief Model, but also included potential response-modifying factors. Predictors for adherent behavior were identified using multiple regression analysis. All predictors were standardized so that regression coefficients (ß) could be compared. Results: Overall median adherence was 0.75 (IQR: 0.5-1.0). Based on a regression model, the following variables were identified as significant in raising adherence: higher age (ß = 0.43, 95%CI: 0.33-0.54), social standards of acceptable behavior (ß = 0.33, 95%CI: 0.27-0.40), subjective/individual assessment of an increased personal health risk (ß = 0.12, 95%CI: 0.05-0.18), self-efficacy (ß = 0.06, 95%CI: 0.02-0.10), female gender (ß = 0.05, 95%CI: 0.01-0.08), and low corona fatigue (behavioral fatigue: ß = -0.11, 95%CI: -0.18 to -0.03). The model showed that such aspects as personal trust in institutions, perceived difficulties in adopting health-promoting measures, and individual assessments of the risk of infection, had no significant influence. Conclusions: This study reveals that several factors significantly influence adherence to measures aimed at controlling the COVID-19 pandemic. To enhance adherence, the government, media, and other relevant stakeholders should take the findings into consideration when formulating policy. By developing social standards and promoting self-efficacy, individuals can influence the behavior of others and contribute toward coping with the pandemic.

Predictors for adherent behavior in the COVID-19 pandemic: A cross-sectional telephone survey

Background During the COVID-19 pandemic, protective measures have been prescribed to prevent or slow down the spread of the SARS-CoV-2 virus and protect the population. Individuals follow these measures to varying degrees. We aimed to identify factors influencing the extent to which protective measures are adhered to. Methods A cross-sectional survey (telephone interviews) was undertaken between April and June 2021 to identify factors influencing the degree to which individuals adhere to protective measures. A representative sample of 1,003 people (age >16 years) in two Austrian states (Carinthia, Vorarlberg) was interviewed. The questionnaire was based on the Health Belief Model, but also included potential response-modifying factors. Predictors for adherent behavior were identified using multiple regression analysis. All predictors were standardized so that regression coefficients (β) could be compared. Results Overall median adherence was 0.75 (IQR: 0.5–1.0). Based on a regression model, the following variables were identified as significant in raising adherence: higher age (β = 0.43, 95%CI: 0.33–0.54), social standards of acceptable behavior (β = 0.33, 95%CI: 0.27–0.40), subjective/individual assessment of an increased personal health risk (β = 0.12, 95%CI: 0.05–0.18), self-efficacy (β = 0.06, 95%CI: 0.02–0.10), female gender (β = 0.05, 95%CI: 0.01–0.08), and low corona fatigue (behavioral fatigue: β = −0.11, 95%CI: −0.18 to −0.03). The model showed that such aspects as personal trust in institutions, perceived difficulties in adopting health-promoting measures, and individual assessments of the risk of infection, had no significant influence. Conclusions This study reveals that several factors significantly influence adherence to measures aimed at controlling the COVID-19 pandemic. To enhance adherence, the government, media, and other relevant stakeholders should take the findings into consideration when formulating policy. By developing social standards and promoting self-efficacy, individuals can influence the behavior of others and contribute toward coping with the pandemic.

Long-term effects of weight-reducing diets in people with hypertension.

BACKGROUND: All major guidelines for antihypertensive therapy recommend weight loss. Dietary interventions that aim to reduce body weight might therefore be a useful intervention to reduce blood pressure and adverse cardiovascular events associated with hypertension. OBJECTIVES: Primary objectives To assess the long-term effects of weight-reducing diets in people with hypertension on all-cause mortality, cardiovascular morbidity, and adverse events (including total serious adverse events, withdrawal due to adverse events, and total non-serious adverse events). Secondary objectives To assess the long-term effects of weight-reducing diets in people with hypertension on change from baseline in systolic blood pressure, change from baseline in diastolic blood pressure, and body weight reduction. SEARCH METHODS: For this updated review, the Cochrane Hypertension Information Specialist searched the following databases for randomised controlled trials up to April 2020: the Cochrane Hypertension Specialised Register, CENTRAL (2020, Issue 3), Ovid MEDLINE, Ovid Embase, and ClinicalTrials.gov. We also contacted authors of relevant papers about further published and unpublished work. The searches had no language restrictions. SELECTION CRITERIA: We included randomised controlled trials (RCTs) of at least 24 weeks' duration that compared weight-reducing dietary interventions to no dietary intervention in adults with primary hypertension. DATA COLLECTION AND ANALYSIS: Two review authors independently assessed risks of bias and extracted data. Where appropriate and in the absence of significant heterogeneity between studies (P > 0.1), we pooled studies using a fixed-effect meta-analysis. In case of moderate or larger heterogeneity as measured by Higgins I2, we used a random-effects model. MAIN RESULTS: This second review update did not reveal any new trials, so the number of included trials remains the same: eight RCTs involving a total of 2100 participants with high blood pressure and a mean age of 45 to 66 years. Mean treatment duration was 6 to 36 months. We judged the risks of bias as unclear or high for all but two trials. No study included mortality as a predefined outcome. One RCT evaluated the effects of dietary weight loss on a combined endpoint consisting of the necessity of reinstating antihypertensive therapy and severe cardiovascular complications. In this RCT, weight-reducing diet lowered the endpoint compared to no diet: hazard ratio 0.70 (95% confidence interval (CI) 0.57 to 0.87). None of the trials evaluated adverse events as designated in our protocol. The certainty of the evidence was low for a blood pressure reduction in participants assigned to weight-loss diets as compared to controls: systolic blood pressure: mean difference (MD) -4.5 mm Hg (95% CI -7.2 to -1.8 mm Hg) (3 studies, 731 participants), and diastolic blood pressure: MD -3.2 mm Hg (95% CI -4.8 to -1.5 mm Hg) (3 studies, 731 participants). We judged the certainty of the evidence to be high for weight reduction in dietary weight loss groups as compared to controls: MD -4.0 kg (95% CI -4.8 to -3.2) (5 trials, 880 participants). Two trials used withdrawal of antihypertensive medication as their primary outcome. Even though we did not consider this a relevant outcome for our review, the results of these RCTs strengthen the finding of a reduction of blood pressure by dietary weight-loss interventions. AUTHORS' CONCLUSIONS: In this second update, the conclusions remain unchanged, as we found no new trials. In people with primary hypertension, weight-loss diets reduced body weight and blood pressure, but the magnitude of the effects are uncertain due to the small number of participants and studies included in the analyses. Whether weight loss reduces mortality and morbidity is unknown. No useful information on adverse effects was reported in the relevant trials.

Antikörpertests bei COVID-19 - Was uns die Ergebnisse sagen

Zusammenfassung Hintergrund Im Rahmen der SARS-CoV-2 (Schweres Akutes Respiratorisches Syndrom Coronavirus 2) Pandemie wird dem Nachweis von virusspezifischen Antikörpern (AK) zunehmend eine wichtige Rolle eingeräumt. Potenziell können aus dem Vorhandensein oder Fehlen von solchen Antikörpern Überlegungen zu Infektion oder Immunität einer Person angestellt werden. Fragestellung Wie sicher kann von positiven bzw. negativen AK Testresultaten auf das tatsächliche Vorhandensein von SARS-CoV-2-spezifischen Antikörpern geschlossen werden? Methodik Darstellung der Wahrscheinlichkeit, mit der bei positiven bzw. negativen Testresultaten tatsächlich AK vorliegen bzw. nicht vorliegen, abhängig von der Vortestwahrscheinlichkeit (Prävalenz von SARS-CoV-2 Infektion) und der Testeigenschaften. Ergebnisse Sensitivität und Spezifität unterschiedlicher SARS-CoV-2 AK Testsysteme variieren zwischen 53% und 94% bzw. zwischen 91% und 99,5%. Bei Anwendung eines Tests mit hoher Testgüte liegt bei einer Vortestwahrscheinlichkeit von 1% bis 5%, wie sie derzeit für die allgemeine Bevölkerung in Österreich oder Deutschland angenommen werden kann, der positive Vorhersagewert (PPW) bei 42% bzw. 79%. Bei Personen mit einer erhöhten Vortestwahrscheinlichkeit von 20%, z.B. Personen aus Risikoberufen, liegt der PPW bei 95%. Bei einer Vortestwahrscheinlichkeit von 80% beträgt der PPW nahezu 100%. Der negative prädiktive Wert (NPW) beträgt mindestens 99,7% für Personen mit einer niedrigen Vortestwahrscheinlichkeit von bis zu 5% und 79% für Personen mit einer Vortestwahrscheinlichkeit von 80%. Bei Anwendung von Testsystemen mit geringerer Sensitivität und Spezifität nimmt die Aussagesicherheit beträchtlich ab. So liegt der PPW bei einer 1%iger Vortestwahrscheinlichkeit dann bei 6%. Schlussfolgerungen Eine ausreichend hohe Sensitivität und Spezifität sind Voraussetzung für eine Anwendung von AK Testsystemen. Bei geringer Vortestwahrscheinlichkeit sind positive Testresultate häufig falsch. Abhängig von der anzunehmenden Prävalenz für eine SARS-CoV-2 Infektion zeigen sich wesentliche Unterschiede in der Bedeutung eines konkreten Testresultats für die jeweils betroffenen Personen. Introduction In the context of the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic, the detection of virus-specific antibodies (AB) will play an increasing role. The presence or absence of such antibodies can potentially lead to considerations regarding immunity and infection. Issue How reliable are inferences from positive or negative test results regarding the actual presence of SARS-CoV-2 specific antibodies? Methods Calculation of the probability that, depending on the pretest probability (prevalence of SARS-CoV-2 infection) and test properties, antibodies are present or absent in the case of positive or negative test results. Results Sensitivity and specificity of different SARS-CoV-2 AB test systems vary between 53 % and 94 % and between 91 % and 99.5 %, respectively. When using a test with high test quality, the positive predictive value (PPV) is 42 % and 7 9%, respectively, with a pre-test probability of 1 % to 5 %, as can currently be assumed for the general population in Austria or Germany. For persons with an increased pre-test probability of 20 %, e. g. persons from high-risk professions, the PPW is 95 %, with a pre-test probability of 80 % the PPW is almost 100 %. The negative predictive value (NPV) is at least 99.7 % for persons with a low pre-test probability of up to 5 % and 79.1 % for persons with a pre-test probability of 80 %. When using test systems with lower sensitivity and specificity, the reliability of the results decreases considerably. The PPV is 5.9 % with a pre-test probability of 1 %. Conclusions A sufficiently high sensitivity and specificity are prerequisites for the application of antibody test systems. Positive test results are often false if the pre-test probability is low. Depending on the assumed prevalence of a SARS-CoV-2 infection, there are substantial differences i the significance of a concrete test result for the respective affected persons.