scholarly journals Prediction of epidemic cholera due toVibrio choleraeO1 in children younger than 10 years using climate data in Bangladesh

2007 ◽  
Vol 136 (1) ◽  
pp. 73-79 ◽  
Author(s):  
F. MATSUDA ◽  
S. ISHIMURA ◽  
Y. WAGATSUMA ◽  
T. HIGASHI ◽  
T. HAYASHI ◽  
...  
Keyword(s):  
Vibrio Cholerae ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Dhaka City ◽  
Climate Variables ◽  
Climate Data ◽  
Number Of Children ◽  
Number Of Patients ◽  
Autoregression Model

SUMMARYTo determine if a prediction of epidemic cholera using climate data can be made, we performed autoregression analysis using the data recorded in Dhaka City, Bangladesh over a 20-year period (1983–2002) comparing the number of children aged <10 years who were infected withVibrio choleraeO1 to the maximum and minimum temperatures and rainfall. We formulated a simple autoregression model that predicts the monthly number of patients using earlier climate variables. The monthly number of patients predicted by this model agreed well with the actual monthly number of patients where the Pearson's correlation coefficient was 0·95. Arbitrarily defined, 39·4% of the predicted numbers during the study period were within 0·8–1·2 times the observed numbers. This prediction model uses the climate data recorded 2–4 months before. Therefore, our approach may be a good basis for establishing a practical early warning system for epidemic cholera.

Cohort study to test the predictability of the NHS Institute for Innovation and Improvement Paediatric Early Warning System

2016 ◽  
Vol 101 (6) ◽  
pp. 552-555 ◽  
Author(s):  
B W Mason ◽  
E D Edwards ◽  
A Oliver ◽  
C V E Powell
Keyword(s):  
Cohort Study ◽  
Early Warning ◽  
Predictive Value ◽  
Early Warning System ◽  
Characteristic Curve ◽  
Positive Likelihood Ratio ◽  
Warning System ◽  
University Hospital ◽  
Number Of Children ◽  
High Dependency

ObjectiveTo test the predictability of the National Health Service Institute for Innovation and Improvement (NHSIII) Paediatric Early Warning System (PEWS) score to identify children at risk of developing critical illness.DesignCohort study.SettingAdmissions to all paediatric wards at the University Hospital of Wales between 1 December 2005 and 30 November 2006.Outcome measuresUnscheduled paediatric high dependency unit (PHDU) admission, paediatric intensive care unit (PICU) admission and death.ResultsThere were 9075 clinical observations from 1000 children. An NHSIII PEWS score of 2 or more, which triggers review, has a sensitivity of 73.2% (95% CI 62.2% to 82.4%), specificity of 75.2% (95% CI 74.3% to 76.1%), positive predictive value (PPV) of 2.6% (95% CI 2.0% to 3.4%), negative predictive value of 99.7% (95% CI 99.5% to 99.8%) and positive likelihood ratio of 3.0 (95% CI 2.6 to 3.4) for predicting PHDU admission, PICU admission or death. Six (37.5%) of the 16 children with an adverse outcome did not have an abnormal NHSIII PEWS score. The area under the receiver operating characteristic curve for the NHSIII PEWS score was 0.83 (95% CI 0.77 to 0.88).ConclusionsThe NHSIII PEWS has a low PPV and its full implementation would result in a large number of false positive triggers. The issue with PEWS scores or triggers is neither their sensitivity nor children with high scores which require clinical interventions who are not ‘false positives’; but their low specificity and low PPV arising from the large number of children with low but raised scores.

scholarly journals Prediction of Dengue Incidence in the Northeast Malaysia Based on Weather Data Using the Generalized Additive Model

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Afiqah Syamimi Masrani ◽  
Nik Rosmawati Nik Husain ◽  
Kamarul Imran Musa ◽  
Ahmad Syaarani Yasin
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Generalized Additive Model ◽  
Additive Model ◽  
Warning System ◽  
Weather Data ◽  
Maximum Temperature ◽  
Climate Data ◽  
Weather Variables ◽  
Relevant Variable

Introduction. Dengue, a vector-borne viral illness, shows worldwide widening spatial distribution beyond its point of origination, namely, the tropical belt. The persistent hyperendemicity in Malaysia has resulted in the formation of the dengue early warning system. However, weather variables are yet to be fully utilized for prevention and control activities, particularly in east-coast peninsular Malaysia where limited studies have been conducted. We aim to provide a time-based estimate of possible dengue incidence increase following weather-related changes, thereby highlighting potential dengue outbreaks. Method. All serologically confirmed dengue patients in Kelantan, a northeastern state in Malaysia, registered in the eDengue system with an onset of disease from January 2016 to December 2018, were included in the study with the exclusion of duplicate entry. Using a generalized additive model, climate data collected from the Kota Bharu weather station (latitude 6°10 ′ N, longitude 102°18 ′ E) was analysed with dengue data. Result. A cyclical pattern of dengue cases was observed with annual peaks coinciding with the intermonsoon period. Our analysis reveals that maximum temperature, mean temperature, rainfall, and wind speed have a significant nonlinear effect on dengue cases in Kelantan. Our model can explain approximately 8.2% of dengue incidence variabilities. Conclusion. Weather variables affect nearly 10% of the dengue incidences in Northeast Malaysia, thereby making it a relevant variable to be included in a dengue early warning system. Interventions such as vector control activities targeting the intermonsoon period are recommended.

scholarly journals Implementation of an Electronic National Early Warning System to Decrease Clinical Deterioration in Hospitalized Patients at a Tertiary Medical Center

2021 ◽  
Vol 18 (9) ◽  
pp. 4550
Author(s):  
Chieh-Liang Wu ◽  
Chen-Tsung Kuo ◽  
Sou-Jen Shih ◽  
Jung-Chen Chen ◽  
Ying-Chih Lo ◽  
...  
Keyword(s):  
Information System ◽  
Early Warning ◽  
Early Warning System ◽  
Medical Center ◽  
Warning System ◽  
Clinical Deterioration ◽  
Early Warning Score ◽  
Control Group ◽  
Number Of Patients ◽  
The Impact

The National Early Warning Score (NEWS) is an early warning system that predicts clinical deterioration. The impact of the NEWS on the outcome of healthcare remains controversial. This study was conducted to evaluate the effectiveness of implementing an electronic version of the NEWS (E-NEWS), to reduce unexpected clinical deterioration. We developed the E-NEWS as a part of the Health Information System (HIS) and Nurse Information System (NIS). All adult patients admitted to general wards were enrolled into the current study. The “adverse event” (AE) group consisted of patients who received cardiopulmonary resuscitation (CPR), were transferred to an intensive care unit (ICU) due to unexpected deterioration, or died. Patients without AE were allocated to the control group. The development of the E-NEWS was separated into a baseline (October 2018 to February 2019), implementation (March to August 2019), and intensive period (September. to December 2019). A total of 39,161 patients with 73,674 hospitalization courses were collected. The percentage of overall AEs was 6.06%. Implementation of E-NEWS was associated with a significant decrease in the percentage of AEs from 6.06% to 5.51% (p = 0.001). CPRs at wards were significantly reduced (0.52% to 0.34%, p = 0.012). The number of patients transferred to the ICU also decreased significantly (3.63% to 3.49%, p = 0.035). Using multivariate analysis, the intensive period was associated with reducing AEs (p = 0.019). In conclusion, we constructed an E-NEWS system, updating the NEWS every hour automatically. Implementing the E-NEWS was associated with a reduction in AEs, especially CPRs at wards and transfers to ICU from ordinary wards.

MODIFIKASI ASESMEN EARLY WARNING SYSTEM UPAYA PENINGKATAN PENERAPAN KESELAMATAN PASIEN

2020 ◽  
Vol 6 (2) ◽  
pp. 112
Author(s):  
Veronika Hutabarat ◽  
Enie Novieastari ◽  
Satinah Satinah
Keyword(s):  
Action Research ◽  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Planning Action

Salah satu faktor dalam meningkatkan penerapan keselamatan pasien adalah ketersediaan dan efektifitas prasarana dalam rumah sakit. Early warning system (EWS) merupakan prasarana dalam mendeteksi perubahan dini  kondisi pasien. Penatalaksanaan EWS masih kurang efektif karena parameter dan nilai rentang scorenya belum sesuai dengan kondisi pasien. Tujuan penulisan untuk mengidentifikasi efektifitas EWS dalam penerapan keselamatan pasien. Metode penulisan action research melalui proses diagnosa, planning action, intervensi, evaluasi dan  refleksi. Responden dalam penelitian ini adalah  perawat yang bertugas di area respirasi dan pasien dengan kasus kompleks respirasi di Rumah Sakit Pusat Rujukan Pernapasan Persahabatan Jakarta. Analisis masalah dilakukan dengan menggunakan diagram fishbone. Masalah yang muncul belum optimalnya implementasi early warning system dalam penerapan keselamatan pasien. Hasilnya 100% perawat mengatakan REWS membantu mendeteksi kondisi pasien, 97,4 % perawat mengatakan lebih efektif dan 92,3 % perawat mengatakan lebih efesien mendeteksi perubahan kondisi pasien. Modifikasi EWS menjadi REWS lebih efektif dan efesien dilakukan karena disesuaikan dengan jenis dan kekhususan Rumah Sakit dan berdampak terhadap kualitas asuhan keperawatan dalam menerapkan keselamatan pasien. Rekomendasi perlu dilakukan monitoring evaluasi terhadap implementasi t.erhadap implementasi REWS dan pengembangan aplikasi berbasis tehnologi

Development of a Pediatric Early Warning System Using Data-Driven Vital Signs

PEDIATRICS ◽  
2016 ◽  
Vol 137 (Supplement 3) ◽  
pp. 256A-256A
Author(s):  
Catherine Ross ◽  
Iliana Harrysson ◽  
Lynda Knight ◽  
Veena Goel ◽  
Sarah Poole ◽  
...  
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Vital Signs ◽  
Warning System ◽  
Data Driven ◽  
Using Data

KAJIAN KEKUATAN SINYAL RADIO (RSSI) XBEE DALAM RANGKA PEMASANGAN LANDSLIDE EARLY WARNING SYSTEM (LEWS) DI KABUPATEN GARUT, TASIKMALAYA DAN MAJALENGKA

2019 ◽  
Vol 3 (2) ◽  
pp. 88
Author(s):  
Riski Fitriani
Keyword(s):  
Early Warning ◽  
Early Warning System ◽  
Warning System ◽  
Signal Strength ◽  
Received Signal Strength ◽  
Received Signal Strength Indicator ◽  
Landslide Early Warning ◽  
Point To Point

Salah satu inovasi untuk menanggulangi longsor adalah dengan melakukan pemasangan Landslide Early Warning System (LEWS). Media transmisi data dari LEWS yang dikembangkan menggunakan sinyal radio Xbee. Sehingga sebelum dilakukan pemasangan LEWS, perlu dilakukan kajian kekuatan sinyal tersebut di lokasi yang akan terpasang yaitu Garut, Tasikmalaya, dan Majalengka. Kajian dilakukan menggunakan 2 jenis Xbee yaitu Xbee Pro S2B 2,4 GHz dan Xbee Pro S5 868 MHz. Setelah dilakukan kajian, Xbee 2,4 GHz tidak dapat digunakan di lokasi pengujian Garut dan Majalengka karena jarak modul induk dan anak cukup jauh serta terlalu banyak obstacle. Topologi yang digunakan yaitu topologi pair/point to point, dengan mengukur nilai RSSI menggunakan software XCTU. Semakin kecil nilai Received Signal Strength Indicator (RSSI) dari nilai receive sensitivity Xbee maka kualitas sinyal semakin baik. Pengukuran dilakukan dengan meninggikan antena Xbee dengan beberapa variasi ketinggian untuk mendapatkan kualitas sinyal yang lebih baik. Hasilnya diperoleh beberapa rekomendasi tinggi minimal antena Xbee yang terpasang di tiap lokasi modul anak pada 3 kabupaten.

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