Dengue Outbreaks in Khyber Pakhtunkhwa (KPK), Pakistan in 2017: An Integrated Disease Surveillance and Response System (IDSRS)-Based Report

Abdullah, .; Ali, Sher; Salman, Muhammad; Din, Misbahud; Khan, Kachkol; Ahmad, Munib; Khan, Faisal Hayat; Arif, Muhammad

doi:10.21307/pjm-2019-013

Cited by 27 publications

(27 citation statements)

References 23 publications

(13 reference statements)

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“…In our study, we found that around more than half of the patients (55.4%) were older than 35 years, which is consistent with earlier reports from the southern part of the country [ 16 ]. In the hyperendemic areas of India, a large cohort of test-positive dengue patients had a median age of 25 years [interquartile range (IQR) = 16-36 years] [ 17 ].…”

Section: Discussionsupporting

confidence: 92%

Electrolyte Imbalance at Admission Does Not Predict the Length of Stay or Mortality in Dengue-Infected Patients

Rehman

Omair

Memon

et al. 2020

Cureus

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Background A pattern of both clinical and biochemical abnormalities is associated with dengue virus infection (DVI). Among the various DVI-related biochemical defects, electrolyte imbalance is one that can alter the morbidity and mortality among patients. However, there is a dearth of evidence to assess the relationship between electrolyte imbalance and the length of stay or mortality in dengue-infected patients in Pakistan. In the current study, we aimed to investigate the association between electrolyte imbalance at the time of admission and the length of stay and mortality among dengue-infected patients. Methods We conducted a retrospective study at a large tertiary care hospital from November 2018 to November 2019. All patients with known chronic diseases and coinfections or those who were taking diuretics therapies or angiotensin-converting enzyme inhibitors were excluded. Our main exposure of interest was electrolytes imbalance and the outcome measure was the length of stay and mortality. Results A total of 1,008 dengue patients were enrolled with a mean length of stay of 2.56 days. Around 29.3% had hyponatremia and 23.2% had hypokalemia at the time of admission, and 21.9% of patients had a stay beyond three days. In multivariable analysis, hyponatremia [adjusted odds ratios (aOR) = 1.29; 95% confidence interval (CI): 0.59-2.84] and hypokalemia (aOR = 2.36; 95% CI: 0.91-6.10) were not found to be associated with the length of stay. However, patients with high troponin levels at admission had a prolonged stay beyond three days (aOR = 5.74; 95% CI: 2.34-14.11). There was a statistically significant association of creatinine levels (aOR = 14.74; 95% CI: 4.19-15.85) and diabetes mellitus (DM) (aOR = 4.36; 95% CI: 1.21-15.74) with mortality after controlling for potential confounders. Conclusion Electrolyte imbalance at admission is not a predictor of length of stay or fatalities in the hospital among patients with DVI. However, troponin levels at admission can increase hospitalization days whereas DM and renal injury have been found to worsen mortality rates.

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Section: Discussionsupporting

confidence: 92%

Electrolyte Imbalance at Admission Does Not Predict the Length of Stay or Mortality in Dengue-Infected Patients

Rehman

Omair

Memon

et al. 2020

Cureus

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“…With powerful cooperative reaction from government and Nongovernmental Organizations (NGOs), healthcare professionals are helping to prevent further spread of the epidemic [24]. Public awareness about the seasonal epidemic outbreaks, especially regarding dengue fever, actual problem understanding, and the approaches to monitor dengue outbreaks are significant considerations.…”

Section: Related Workmentioning

confidence: 99%

Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

et al. 2021

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Seasonal outbreaks have several different periods that occur primarily during winter in temperate regions, while influenza may occur throughout the year in tropical regions, triggering outbreaks more irregularly. Similarly, dengue occurs in the star of the rainy season in early May and reaches its peak in late June. Dengue and flu brought an impact on various countries in the years 2017–2019 and streaming Twitter data reveals the status of dengue and flu outbreaks in the most affected regions. This research work presents that Social Media Analysis (SMA) can be used as a detector of the epidemic outbreak and to understand the sentiment of social media users regarding various diseases. Providing awareness about seasonal outbreaks through SMA is an effective approach for researchers and healthcare responders to detect the early outbreaks. The proposed model aims to find the sentiment about the disease in tweets, and the seasonal outbreaks-related tweets are classified into two classes as disease positive and disease negative. This work proposes a machine-learning-based approach to detect dengue and flu outbreaks in social media platform Twitter, using four machine learning algorithms: Random Forest (RF), K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Decision Tree (DT), with the help of Term Frequency and Inverse Document Frequency (TF-IDF). For experimental analysis, two datasets (dengue and flu) are analyzed individually. The experimental results show that the RF classifier has outperformed the comparison models in terms of improved accuracy, precision, recall, F1-measure, and Receiver Operating Characteristic (ROC) curve. The proposed work offers favorable performance with total precision, accuracy, recall, and F1-measure ranging from 84% to 88% for conventional machine learning techniques.

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“…Thus, it is an important need of the present day automated surveillance systems to use data from EHR. For example, Integrated Disease Surveillance and Response System (IDSRS) requires data to be obtained from patient medical records [77], [78].…”

Section: B Public Health Surveillancementioning

confidence: 99%

Secondary Use of Electronic Health Record: Opportunities and Challenges

Shah¹,

Khan²

2020

IEEE Access

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In the present technological era, healthcare providers generate huge amounts of clinical data on a daily basis. Generated clinical data is stored digitally in the form of Electronic Health Record (EHR) as a central data repository of hospitals. Data contained in EHR is not only used for the patients' primary care but also for various secondary purposes such as clinical research, automated disease surveillance and clinical audits for quality enhancement. Using EHR data for secondary purposes without consent or in some cases even with consent creates privacy issues. Secondly, EHR data is also made accessible to various stakeholders including different government agencies at various geographical sites through wired or wireless networks. Sharing of EHR across multiple agencies makes it vulnerable to cyber attacks and also makes it difficult to implement strict privacy laws as in some cases data is shared with organization that is governed by specific regional law. Privacy of an individual could be severely affected when their sensitive private information contained in EHR is leaked or exposed to the public. Data leaks can cause financial losses or an individual may encounter social boycott if their medical condition is exposed in public. To protect patients personal data from such threats, there exists different privacy regulations such as General Data Protection Regulation (GDPR), Health Insurance Portability and Accountability Act (HIPAA) and My Health Record (MHR). However, continually evolving state-of-the-art techniques in Machine Learning (ML), Data Analytics (DA) and hacking are making it even more difficult to completely protect an individual's / patient's privacy. In this article, we have systematically examined various secondary uses of EHR with the aim to highlight how these secondary uses affect patients' privacy. Secondly, we have critically analyzed GDPR & HIPAA regulations and highlighted their possible areas of improvement, considering escalating use of technology and different secondary uses of EHR. INDEX TERMS Electronic Health Records (EHR), ethical concerns, General Data Protection Regulation (GDPR), privacy, secondary uses of EHR.

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Dengue Outbreaks in Khyber Pakhtunkhwa (KPK), Pakistan in 2017: An Integrated Disease Surveillance and Response System (IDSRS)-Based Report

Cited by 27 publications

References 23 publications

Electrolyte Imbalance at Admission Does Not Predict the Length of Stay or Mortality in Dengue-Infected Patients

Electrolyte Imbalance at Admission Does Not Predict the Length of Stay or Mortality in Dengue-Infected Patients

Early Detection of Seasonal Outbreaks from Twitter Data Using Machine Learning Approaches

Secondary Use of Electronic Health Record: Opportunities and Challenges

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