Conectar
by Abebaw Misganaw, Alaye Debas Ayenew, Netsanet Temesgen Ayenew, Enyew Fenta Mengistu, Baye Ashenef, Samrawit Nega Shiferaw, Getamesay Demelash Simegn
BackgroundSurgery and anesthesia can disrupt normal physiological function through surgical stress and residual anesthetic effects, increasing the risk of post-anesthetic complications, known as critical incidents. This study aimed to determine the incidence of critical events in the post-anesthesia care unit at Debre Markos Comprehensive Specialized Hospital, Ethiopia.
MethodsAn institution-based prospective cross-sectional study was conducted from June 1, 2024, to September 30, 2024. The sample size was determined by a single proportion formula using a prevalence of 50% and a 5% margin of error at the 95% confidence interval. The data was analyzed using SPSS version 22 for windows. Analysis was conducted using bivariable and multivariable logistic regression as needed.
ResultOf the 422 patients, 160 (37.9%) experienced one or more critical events, with a total of 214 complications recorded. The most common critical events that occurred in the PACU were cardiovascular-related events (42%) and respiratory & airway related incidents (20%). BMI, duration of anesthesia, intraoperative complications, patient handover, PACU staff training, and ASA physical status were significantly associated with the occurrence of critical events. The odds of critical events were higher among underweight (AOR = 3.71; 95% CI: 1.27–10.79) and overweight patients (AOR = 3.05; 95% CI: 1.28–7.24). Anesthesia duration of 1–2 hours (AOR = 2.01; 95% CI: 1.06–3.81) and >2 hours (AOR = 4.11; 95% CI: 1.59–10.66) also increased the risk. Patients with intraoperative complications had higher odds of critical events (AOR = 3.52; 95% CI: 1.88–6.58), as did those without proper handover (AOR = 3.92; 95% CI: 2.11–7.25). Increasing ASA class was associated with higher risk ASA II (AOR = 2.59; 95% CI: 1.11–6.07), ASA III (AOR = 2.86; 95% CI: 1.20–6.86), and ASA IV (AOR = 11.75; 95% CI: 2.76–50.03). Additionally, patients cared for by PACU nurses without prior PACU training were more likely to develop complications (AOR = 3.15; 95% CI: 1.73–5.72).
ConclusionApproximately 38% of patients experienced ≥1 critical event, mainly cardiovascular and respiratory complications. Patients who had intraoperative complications, ASA 2 to ASA 4 status, under/overweight, and those who received anesthesia for a prolonged duration were relatively at higher risk of developing critical events. There was a long time to stay in the PACU for those patients who experienced critical events.
Ethiopia, the second most populous country in Africa, faces significant demographic transitions, with fertility rates playing a central role in shaping economic and healthcare policies. Family planning programmes face challenges due to funding limitations. The recent suspension of the US Agency for International Development funding exacerbates these issues, highlighting the need for accurate birth forecasting to guide policy and resource allocation. This study applied time-series and advanced machine-learning models to forecast future birth trends in Ethiopia.
Secondary data from the Ethiopian Demographic and Health Survey from 2000 to 2019 were used. After data preprocessing steps, including data conversion, filtering, aggregation and transformation, stationarity was checked using the Augmented Dickey-Fuller (ADF) test. Time-series decomposition was then performed, followed by time-series splitting. Seven forecasting models, including Autoregressive Integrated Moving Average, Prophet, Generalised Linear Models with Elastic Net Regularisation (GLMNET), Random Forest and Prophet-XGBoost, were built and compared. The models’ performance was evaluated using key metrics such as root mean square error (RMSE), mean absolute error (MAE) and R-squared value.
GLMNET emerged as the best model, explaining 77% of the variance with an RMSE of 119.01. Prophet-XGBoost performed reasonably well but struggled to capture the full complexity of the data, with a lower R-squared value of 0.32 and an RMSE of 146.87. Forecasts were made for both average monthly births and average births per woman over a 10-year horizon (2025–2034). The forecast for average monthly births indicated a gradual decline over the projection period. Meanwhile, the average births per woman showed an increasing trend but fluctuated over time, influenced by demographic shifts such as changes in fertility preferences, age structure and migration patterns.
This study demonstrates the effectiveness of combining time-series models and machine learning, with GLMNET and Prophet XGBoost emerging as the most effective. While average monthly births are expected to decline due to demographic transitions and migration, the average births per woman will remain high, reflecting persistent fertility preferences within certain subpopulations. These findings underscore the need for policies addressing both population trends and sociocultural factors.
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