Publications by authors named "Pejman Adily"

Objective: To investigate the impact of contextual socioeconomic and demographic characteristics of residents on COVID-19 outcomes during public health measures.

Methods: Aggregated data from the NSW Notifiable Conditions Information Management System linked to Australian Census data, by periods of strict and relaxed measures, were used.

Results: During strict measures, residents of areas of lower socioeconomic status (SES) had a higher risk of infection, with the lowest areas having greater risk compared with the highest areas ((hazard ratio (HR)) 7.

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Background: Short interpregnancy interval (IPI) following caesarean delivery is associated with uterine rupture in subsequent pregnancies. However, the interval required to minimise this risk is unknown. We investigated how the interval between pregnancies and induction or augmentation of labour affect the likelihood of uterine rupture among parturients with one previous livebirth by caesarean delivery who had a subsequent trial of labour.

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Objective: Low acuity presentations (LAPs) contribute to large numbers of ED presentations and carry numerous consequences. The present study sought to improve the understanding of regional infant LAPs by analysing temporal patterns of presentation, discharge diagnoses and potential predictive factors.

Methods: This retrospective observational study examined ED presentations among children less than 12 months old (infants) to the Royal Prince Alfred Hospital between 2017 and 2019.

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Aim: To examine antenatal depression risk screening coverage under the NSW SAFE START Strategic Policy and to explore maternal and sociodemographic factors associated with under-screening.

Methods: Completion rates for the Edinburgh Depression Scale (EDS) were examined in a retrospective dataset of routinely collected antenatal care data including all women who birthed at public health facilities in Sydney Local Health District from 1 October 2019 to 6 August 2020. Potential sociodemographic/clinical factors associated with under-screening were identified using univariate and multivariate logistic regression.

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