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Abstract Background There is a dearth of robust evidence regarding the correlation between psoriasis with maternal and neonatal outcomes, making it challenging to establish definitive recommendations for the management of these patients. This systematic review and meta-analysis aimed to review the evidence with regard to the impact of maternal psoriasis on maternal and neonatal outcomes. Methods Following the PRISMA guideline, a systematic search of English articles using PubMed, Embase, Scopus, ScienceDirect, Web of Science, Google Scholar, and the Cochrane Library was conducted. The search was performed from inception to 22nd of May 2022. Result A significant association was observed between psoriasis and maternal outcomes, including cesarean delivery [OR = 1.25 (95% CI: 1.13–1.30, p-value = 0.001)], (pre)eclampsia [OR = 1.29 (95% CI: 1.15–1.44, p-value = 0.0001)], gestational diabetes [Odds Ratio (OR) = 1.23 (95% Confidence Intervals (CI): 1.15–1.30, p-value = 0.001)], gestational hypertension [OR = 1.31 (95% CI: 1.18–1.45, p-value = 0.001)] and preterm birth [OR = 1.22 (95% CI: 1.10–1.35, p-value = 0.001)]. Also, there was a significant association between psoriasis and neonatal outcomes, including small for gestational age [OR = 1.07 (95% CI: 1.02–1.11, p-value = 0.053)], low birth weight [OR = 1.19 (95% CI: 1.02–1.38, p-value = 0.001)] and stillbirth [OR = 1.27 (95% CI: 1.04–1.55, p-value = 0.023)]. Conclusion Maternal psoriasis could negatively impact maternal and neonatal outcomes. Our results strengthen the importance of close monitoring of the mothers’ psoriasis status before and during pregnancy.

Abstract Background Healthcare-associated infections (HAIs) are a threat to patients. Accurate surveillance is required to identify and prevent HAIs. To estimate the incidence rate, report the accuracy and identify the barriers of reporting HAIs using a mixed-method study. Methods In this quantitative study, we externally evaluated the incidence rate and accuracy of the routine surveillance system in one of the main hospitals by an active follow-up of patients from September to December 2021. We used in-depth interviews with 18 experts to identify the barriers of the routine surveillance system. Results Among 404 hospitalized patients, 88 HAIs were detected. The estimated rate of HAIs was 17.1 (95% Confidence Intervals 95: 14.1, 21.1) per 1000 patient-days follow-up. However, in the same period, 116 HAIs were reported by the routine surveillance system, but the agreement between the two approaches was low (sensitivity = 61.4%, specificity = 82.6%, negative predictive value = 89.7%, and positive predictive validity = 46.5%). The minimum and maximum positive predictive values were observed in urinary tract infection (32.3%) and surgical site infection (60.9%). The main barrier of reporting HAIs was lack of cooperation in reporting HAIs by infection control link nurses and laboratory supervisors. Conclusions The discrepancy between the longitudinal study findings and the routine surveillance might be related to the inaccessibility of the surveillance system to clinical information of patients. In this regard, decreasing the barriers, increasing the knowledge of infection control nurses and other nurses, as well as the development of hospital information systems are necessary.

Abstract Background The concurrent usage of herbal medicines with conventional therapies is an important concern in cancer treatment which can lead to unexpected consequences like herb-drug interactions. This study aimed to determine the prevalence of potential herb-drug interactions and to predict factors associated with herb-drug interactions for cancer patients. Methods This cross-sectional study was conducted among a convenience sample of 315 cancer patients referring to the oncology clinics of Kerman city in 2018. Data were collected via comprehensive face-to-face interviews and medical chart reviews. A drug interaction checker was used to determine herb-drug interactions. The information of patients was compared based on herb-drug interactions using bivariable logistic regression models, and predictors were determined by the multivariable logistic regression model. All analyses were performed by Stata software version 16. Results Of 262 patients (83.2% of the patients) who used herbal medicines, 209 patients [79.8% (95% Confidence Intervals (CI): 75.2 – 85.1)] had potential herb-drug interactions. Chamomile was the most popular herbal medicine (n = 163, 78.0%), and minor and moderate herb-drug interactions were caused by green tea (n = 34, 16.3%) and peppermint (n = 78, 37.5%). The number of chemotherapeutic agents (OR: 1.92, 95% CI: 1.43–2.58; P-value