Comparing malaria early detection methods in a declining transmission setting in northwestern Ethiopia.

BACKGROUND: Despite remarkable progress in the reduction of malaria incidence, this disease remains a public health threat to a significant portion of the world's population. Surveillance, combined with early detection algorithms, can be an effective intervention strategy to inform timely public health responses to potential outbreaks. Our main objective was to compare the potential for detecting malaria outbreaks by selected event detection methods. METHODS: We used historical surveillance data with weekly counts of confirmed Plasmodium falciparum (including mixed) cases from the Amhara region of Ethiopia, where there was a resurgence of malaria in 2019 following several years of declining cases. We evaluated three methods for early detection of the 2019 malaria events: 1) the Centers for Disease Prevention and Control (CDC) Early Aberration Reporting System (EARS), 2) methods based on weekly statistical thresholds, including the WHO and Cullen methods, and 3) the Farrington methods. RESULTS: All of the methods evaluated performed better than a naïve random alarm generator. We also found distinct trade-offs between the percent of events detected and the percent of true positive alarms. CDC EARS and weekly statistical threshold methods had high event sensitivities (80-100% CDC; 57-100% weekly statistical) and low to moderate alarm specificities (25-40% CDC; 16-61% weekly statistical). Farrington variants had a wide range of scores (20-100% sensitivities; 16-100% specificities) and could achieve various balances between sensitivity and specificity. CONCLUSIONS: Of the methods tested, we found that the Farrington improved method was most effective at maximizing both the percent of events detected and true positive alarms for our dataset (> 70% sensitivity and > 70% specificity). This method uses statistical models to establish thresholds while controlling for seasonality and multi-year trends, and we suggest that it and other model-based approaches should be considered more broadly for malaria early detection.

Prevalence and Associated Factors of Perinatal Asphyxia in Neonates Admitted to Ayder Comprehensive Specialized Hospital, Northern Ethiopia: A Cross-Sectional Study.

BACKGROUND: Perinatal asphyxia is defined as the inability of the newborn to initiate and sustain enough respiration after delivery and is characterized by a marked impairment of gas exchange. It is one of the most common causes of neonatal mortality and morbidity. There are very few studies on perinatal asphyxia in Tigray, and so this study is aimed at assessing the prevalence and associated factors of perinatal asphyxia in Ayder Comprehensive Specialized Hospital NICU, Tigray, Ethiopia. METHODS: An institution-based cross-sectional study design was conducted among neonates admitted to Ayder Comprehensive Specialized Hospital from January 1, 2016, to December 30, 2017. Medical records of 267 neonates admitted to the neonatal intensive care unit were selected by a systematic sampling method, and relevant information was collected using a checklist. The data was analyzed using SPSS version 20. Descriptive statistics were computed to determine the prevalence of birth asphyxia and sociodemographic and obstetrics data. Binary logistic regression was used to test associations between the associated factors and perinatal asphyxia. First bivariate analysis was performed to assess the association without controlling the effect of other independent variables. Variables with P value < 0.25 were fitted to the multivariable binary logistic regression model. Finally, variables with P value < 0.25 were fitted to the multivariable binary logistic regression model. Finally, variables with. RESULTS: Of the 267 neonates, 48 neonates had perinatal asphyxia, giving a prevalence of 18%. Prolonged labor (AOR = 5.19, 95% CI: 1.73-15.63, P value < 0.25 were fitted to the multivariable binary logistic regression model. Finally, variables with P value < 0.25 were fitted to the multivariable binary logistic regression model. Finally, variables with P value < 0.25 were fitted to the multivariable binary logistic regression model. Finally, variables with Conclusion and Recommendations. Prevalence and mortality of asphyxia were high. Prolonged labor, presence of meconium, and preeclampsia were determinant factors for birth asphyxia. Early detection and intervention of high-risk mothers should be carried out by health care providers, and mothers should be monitored with partograph during labor.