ABSTRACT
Background: Healthcare inequity has widely affected marginalized and immigrant communities globally during the COVID-19 pandemic. Aims: This study assessed the effect of COVID-19 pandemic on health care delivery to immigrant populations in Isfahan Province, Islamic Republic of Iran. Methods: All 67 hospitals across Isfahan Province were included in this study conducted from 1 March to 31 May 2020. Data on clinical manifestations, comorbidities, patient management, and outcomes of patients during hospital admission were extracted from medical records and analysed using SPSS for chi-square and odds ratio (OR). Results: One hundred and sixty-eight (3.3%) of 5128 PCR-confirmed COVID-19 cases during the study period were immigrants and were included in the study. There were no differences in sex, clinical presentation, comorbidities, and length of hospital stay between the non-immigrant and immigrant groups. Immigrant patients were significantly younger and had poorer outcomes, including tracheal intubation [OR = 1.9, 95% confidence interval (CI): 1.2–3.1); P = 0.009] and in-hospital mortality (OR = 1.6; 95% CI: 1.1–2.4; P = 0.02). Conclusion: Adverse health outcomes among immigrant communities may be an indication of health inequity and should be addressed by the relevant policymakers.
Subject(s)
COVID-19 , Betacoronavirus , Disease Outbreaks , Emigrants and Immigrants , IranABSTRACT
ObjectivePatients, families and community members would like emergency department wait time visibility. This would improve patient journeys through emergency medicine. The study objective was to derive, internally and externally validate machine learning models to predict emergency patient wait times that are applicable to a wide variety of emergency departments. MethodsTwelve emergency departments provided three years of retrospective administrative data from Australia (2017-19). Descriptive and exploratory analyses were undertaken on the datasets. Statistical and machine learning models were developed to predict wait times at each site and were internally and externally validated. Model performance was tested on COVID-19 period data (January to June 2020). ResultsThere were 1,930,609 patient episodes analysed and median site wait times varied from 24 to 54 minutes. Individual site model prediction median absolute errors varied from +/-22.6 minutes (95%CI 22.4,22.9) to +/- 44.0 minutes (95%CI 43.4,44.4). Global model prediction median absolute errors varied from +/-33.9 minutes (95%CI 33.4, 34.0) to +/-43.8 minutes (95%CI 43.7, 43.9). Random forest and linear regression models performed the best, rolling average models under-estimated wait times. Important variables were triage category, last-k patient average wait time, and arrival time. Wait time prediction models are not transferable across hospitals. Models performed well during the COVID-19 lockdown period. ConclusionsElectronic emergency demographic and flow information can be used to approximate emergency patient wait times. A general model is less accurate if applied without site specific factors. What is already known on this subject Patients and families want to know approximate emergency wait times, which will improve their ability to manage their logistical, physical and emotional needs whilst waiting There are a few small studies from a limited number of jurisdictions, reporting model methods, important predictor variables and accuracy of derived models What this study adds Our study demonstrates that predicting wait times from simple, readily available data is complex and provides estimates that arent as accurate as patients would like, however rough estimates may still be better than no information We present the most influential variables regarding wait times and advise against using rolling average models, preferring random forest or linear regression techniques Emergency medicine machine learning models may be less generalisable to other sites than we hope for when we read manuscripts or buy commercial off-the-shelf models or algorithms. Models developed for one site lose accuracy at another site and global models built for whole systems may need customisation to each individual site. This may apply to data science clinical decision instruments as well as operational machine learning models.
