Background Although the context of parenting has been incorporated into psychosocial pain research, very little attention has been paid to how parenting styles influence chronic pain in adolescents. The present study aimed to investigate the mediating role of self-esteem, emotional intelligence, and psychological distress in the association between parenting styles and chronic pain. Method Seven hundred and thirty nine adolescents and their parents participated in this study. To identify adolescents with chronic pain, screening questions based on the 11th revision of the International Classification of Diseases were used. Baumrind parenting style questionnaire was used to assess the parenting style (permissive, authoritarian, and authoritative parenting styles). The structural equation modelling (SEM) was carried out in M-Plus version 6 to evaluate the direct, indirect, and total effects of different parenting styles on chronic pain. Results The results in the SEM models revealed that only the indirect paths from authoritative and authoritarian parenting styles to pain through emotional intelligence (βauthoritative = − 0.003, 95% CI = − 0.008 to − 0.003; βauthoritarian = 0.001, 95% CI = 0.001 to 0.003) and psychological distress (βauthoritative = − 0.010, 95% CI = − 0.021 to − 0.004; βauthoritarian = 0.008, 95% CI = 0.004 to 0.016) were significant. Indirect paths from permissive style to pain and the mediating role of self-esteem were not significant. Discussion Emotional intelligence and psychological distress significantly mediated the effects of authoritative and authoritarian parenting styles on chronic pain. The current results support the notion that interventions targeting effective parent–adolescent communication may be an important part of chronic pain management in adolescents. Moreover, the results provide rationale for targeting emotional intelligence and psychological distress in adolescents by explicitly teaching effective communication skills, expressing opinions and minds, and emotion regulation strategies.

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Background Corona Virus Disease 2019 (COVID-19) presentations range from those similar to the common flu to severe pneumonia resulting in hospitalization with significant morbidity and/or mortality. In this study, we made an attempt to develop a predictive scoring model to improve the early detection of high risk COVID-19 patients by analyzing the clinical features and laboratory data available on admission. Methods We retrospectively included 480 consecutive adult patients, aged 21–95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were collected from the medical records and analyzed using multiple logistic regression analysis. The final data analysis was utilized to develop a simple scoring model for the early prediction of mortality in COVID-19 patients. The score given to each associated factor was based on the coefficients of the regression analyses. Results A novel mortality risk score (COVID-19 BURDEN) was derived, incorporating risk factors identified in this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84–90%, and less than 84%), increased PT (> 16.2 s), diastolic blood pressure (≤ 75 mmHg), BUN (> 23 mg/dL), and raised LDH (> 731 U/L) were the features constituting the scoring system. The patients are triaged to the groups of low- (score < 4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting mortality in patients with a score of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively. Conclusions Using this scoring system in COVID-19 patients, the patients with a higher risk of mortality can be identified which will help to reduce hospital care costs and improve its quality and outcome.

Background: Corona Virus Disease 2019 (COVID-19) presentation resembles common flu or can be more severe; it can result in hospitalization with significant morbidity and/or mortality. We made an attempt to develop a predictive model and a scoring system to improve the diagnostic efficiency for COVID-19 mortality via analysis of clinical features and laboratory data on admission. Methods: We retrospectively enrolled 480 consecutive adult patients, aged 21-95, who were admitted to Faghihi Teaching Hospital. Clinical and laboratory features were extracted from the medical records and analyzed using multiple logistic regression analysis. Results: A novel mortality risk score (COVID-19 BURDEN) was calculated, incorporating risk factors from this cohort. CRP (> 73.1 mg/L), O2 saturation variation (greater than 90%, 84-90%, and less than 84%), increased PT (>16.2s), diastolic blood pressure (≤75 mmHg), BUN (>23 mg/dL), and raised LDH (>731 U/L) are the features comprising the scoring system. The patients are triaged to the groups of low- (score <4) and high-risk (score ≥ 4) groups. The area under the curve, sensitivity, and specificity for predicting non-response to medical therapy with scores of ≥ 4 were 0.831, 78.12%, and 70.95%, respectively. Conclusion: Using this scoring system in COVID-19 patients, the severity of the disease will be determined in the early stages of the disease, which will help to reduce hospital care costs and improve its quality and outcome.