Estimating antibiotics consumption in a tertiary care hospital in Islamabad using a WHO's defined daily dose methodology.

BACKGROUND: Antibiotics have helped to reduce the incidence of common infectious diseases in all modern healthcare systems, but improper use of antibiotics including their overuse and misuse can change the bacteria so much that antibiotics don't work against them. In case of developing imposable selective pressure with regard to the proportion of hospitalized patients who receive antibiotics, the quantity of antibiotics that are prescribed to them, and the proportion of patients who receive antibiotic treatment is one of the major contributors to the rising global health issue of antimicrobial resistance. Concerning the levels of antibiotic consumption in Pakistani hospitals, there is negligible research data available. AIM: This study aimed to evaluate five-year inpatient antibiotic use in a tertiary care hospital in Islamabad using the World Health Organization (WHO) Recommended Anatomical Therapeutic Chemical (ATC) Classification / Defined Daily Dose (DDD) methodology. METHOD: It was a descriptive study involving a retrospective record review of pharmacy records of antibiotics dispensed (amount in grams) to patients across different specialties of the hospital from January 2017 to December 2021 (i.e., 60 consecutive months). The antibiotic consumption was calculated by using the DDD/100-Bed Days (BDs) formula, and then relative percent change was estimated using Microsoft Excel 2021 edition. RESULT: A total of 148,483 (77%) patients who received antibiotics were included in the study out of 193,436 patients admitted in the hospital. Antibiotic consumption trends showed considerable fluctuations over a five-year period. It kept on declining irregularly from 2017 to 2019, inclined vigorously in 2020, and then suddenly dropped to the lowest DDD/100 BDs value (96.02) in the last year of the study. The overall percentage of encounters in which antibiotics were prescribed at tertiary care hospital was 77% which is very high compared to the WHO standard reference value (< 30%). WATCH group antibiotics were prescribed (76%) and consumed more within inpatient settings than Access (12%) and Reserve (12%) antibiotics. CONCLUSION: The hospital antibiotic consumption data is well maintained across different inpatient specialties but it is largely non-aligned with WHO AWaRe (Access-Watch-Reserve) antibiotics use and optimization during 2017-2021. Compared to the WHO standard reference figure, the overall percentage of antibiotics encountered was higher by about 47%. Antibiotic consumption trends vary with a slight increase in hospital occupancy rate, with positive relative changes being lower in number but higher in proportion than negative changes. Although the hospital antibiotics policy is in place but seems not to be followed with a high degree of adherence.

Prescription drug dependence with and without concurrent illicit drug use: a multicenter cross-sectional survey among an addiction treatment seeking population.

Background: Dependence on prescription drugs and illicit drugs imposes a global health and social burden. Despite accumulating evidence of prescription drugs and illicit drugs dependence, none of the systematized studies has explored the magnitude of this problem in Pakistan. The aim is to investigate the extent and associated factors of prescription drug dependence (PDD), as opposed to concomitant prescription drug dependence and illicit drug use (PIDU), within a sample of individuals seeking addiction treatment. Methods: The cross sectional study was conducted on the sample recruited from three drug treatment centers in Pakistan. Face-to-face interviews were conducted with participants who met ICD-10 criteria for prescription drug dependence. Several aspects like substance use histories, negative health outcomes, patient attitude, pharmacy and physician practices also collected to predict the determinants of (PDD). Binomial logistic regression models examined the factors associated with PDD and PIDU. Results: Of the 537 treatment seeking individuals interviewed at baseline, close to one third (178, 33.3%) met criteria for dependence on prescription drugs. The majority of the participants were male (93.3%), average age of 31 years, having urban residence (67.4%). Among participants who met criteria for dependence on prescription drugs (71.9%), reported benzodiazepines as the most frequently used drug, followed by narcotic analgesics (56.8%), cannabis/marijuana (45.5%), and heroin (41.5%). The patients reported alprazolam, buprenorphine, nalbuphine, and pentazocin use as alternatives to illicit drugs. PDD was significantly negatively associated with injectable route (OR = 0.281, 95% CI, 0.079-0.993) and psychotic symptoms (OR = 0.315, 95% CI, 0.100, 0.986). This implies that PDD is less likely to be associated with an injectable route and psychotic symptoms in contrast to PIDU. Pain, depression and sleep disorder were primary reasons for PDD. PDD was associated with the attitude that prescription drugs are safer than illicit drugs (OR = 4.057, 95%CI, 1.254-13.122) and PDD was associated with being on professional terms (i.e., having an established relationship) with pharmaceutical drugs retailers for acquisition of prescription drugs. Discussion and conclusion: The study found benzodiazepine and opioid dependence in sub sample of addiction treatment seekers. The results have implications for drug policy and intervention strategies for preventing and treating drug use disorders.

Modelling Electricity Consumption During the COVID19 Pandemic: Datasets, Models, Results and a Research Agenda.

The COVID19 pandemic has impacted the global economy, social activities, and Electricity Consumption (EC), affecting the performance of historical data-based Electricity Load Forecasting (ELF) algorithms. This study thoroughly analyses the pandemic's impact on these models and develop a hybrid model with better prediction accuracy using COVID19 data. Existing datasets are reviewed, and their limited generalization potential for the COVID19 period is highlighted. A dataset of 96 residential customers, comprising 36 and six months before and after the pandemic, is collected, posing significant challenges for current models. The proposed model employs convolutional layers for feature extraction, gated recurrent nets for temporal feature learning, and a self-attention module for feature selection, leading to better generalization for predicting EC patterns. Our proposed model outperforms existing models, as demonstrated by a detailed ablation study using our dataset. For instance, it achieves an average reduction of 0.56% & 3.46% in MSE, 1.5% & 5.07% in RMSE, and 11.81% & 13.19% in MAPE over the pre- and post-pandemic data, respectively. However, further research is required to address the varied nature of the data. These findings have significant implications for improving ELF algorithms during pandemics and other significant events that disrupt historical data patterns.

Potential and weak links in the management of tuberculosis by Pakistani private pharmacy staff.

Introduction: The emergence of MDR-TB is a global threat and an obstacle to the effective control of TB in Pakistan. A lack of proper TB knowledge among the staff in private pharmacies and the sale of compromised quality anti-TB drugs are the main instigators of multidrug-resistant tuberculosis (MDR-TB). Thus, this study was aimed at investigating the quality and storage conditions of fixed-dose combination (FDC) anti-TB drugs along with the awareness of staff working in private pharmacies regarding the identification of potential patients with TB and dispensing the inappropriate treatment regimens contributing to MDR-TB. Methods: The study is completed in two phases. In phase I a cross-sectional study is performed using two quantitative research designs, i.e., exploratory and descriptive, to evaluate the knowledge of private pharmacy staff. The sample of 218 pharmacies was selected. While in phase II cross sectional survey is conducted in 10 facilities from where FDC anti TB drugs were sampled for analyzing their quality. Result: Results revealed the presence of pharmacists only at 11.5% of pharmacies. Approximately 81% of staff at pharmacies had no awareness of MDR-TB, while 89% of pharmacies had no TB-related informative materials. The staff identified that most of the patients with TB (70%) were of poor socio-economic class, which restricted their purchase of four FDCs only up to 2-3 months. Only 23% were acquainted with the Pakistan National TB Program (NTP). Except for MDR-TB, the results showed a significant correlation between the experiences of staff with TB awareness. Findings from the quality evaluation of four FDC-TB drugs indicated that the dissolution and content assay of rifampicin were not according to the specifications, and overall, 30% of samples failed to comply with specifications. However, the other quality attributes were within the limits. Conclusion: In light of the data, it can be concluded that private pharmacies could be crucial to the effective management of NTP through the timely identification of patients with TB, appropriate disease and therapy-related education and counseling, and proper storage and stock maintenance.

Multi-Stage Temporal Convolution Network for COVID-19 Variant Classification.

The outbreak of the novel coronavirus disease COVID-19 (SARS-CoV-2) has developed into a global epidemic. Due to the pathogenic virus's high transmission rate, accurate identification and early prediction are required for subsequent therapy. Moreover, the virus's polymorphic nature allows it to evolve and adapt to various environments, making prediction difficult. However, other diseases, such as dengue, MERS-CoV, Ebola, SARS-CoV-1, and influenza, necessitate the employment of a predictor based on their genomic information. To alleviate the situation, we propose a deep learning-based mechanism for the classification of various SARS-CoV-2 virus variants, including the most recent, Omicron. Our model uses a neural network with a temporal convolution neural network to accurately identify different variants of COVID-19. The proposed model first encodes the sequences in the numerical descriptor, and then the convolution operation is applied for discriminative feature extraction from the encoded sequences. The sequential relations between the features are collected using a temporal convolution network to classify COVID-19 variants accurately. We collected recent data from the NCBI, on which the proposed method outperforms various baselines with a high margin.

Vision Transformer and Deep Sequence Learning for Human Activity Recognition in Surveillance Videos.

Human Activity Recognition is an active research area with several Convolutional Neural Network (CNN) based features extraction and classification methods employed for surveillance and other applications. However, accurate identification of HAR from a sequence of frames is a challenging task due to cluttered background, different viewpoints, low resolution, and partial occlusion. Current CNN-based techniques use large-scale computational classifiers along with convolutional operators having local receptive fields, limiting their performance to capture long-range temporal information. Therefore, in this work, we introduce a convolution-free approach for accurate HAR, which overcomes the above-mentioned problems and accurately encodes relative spatial information. In the proposed framework, the frame-level features are extracted via pretrained Vision Transformer; next, these features are passed to multilayer long short-term memory to capture the long-range dependencies of the actions in the surveillance videos. To validate the performance of the proposed framework, we carried out extensive experiments on UCF50 and HMDB51 benchmark HAR datasets and improved accuracy by 0.944% and 1.414%, respectively, when compared to state-of-the-art deep models.

Mutation screening of multiple Pakistani MCPH families revealed novel and recurrent protein-truncating mutations of ASPM.

Autosomal primary microcephaly (MCPH) is a heterogenetic disorder that affects brain's cerebral cortex size and leads to a reduction in the cranial vault. Along with the hallmark feature of reduced head circumference, microcephalic patients also exhibit a variable degree of intellectual disability as well. Genetic studies have reported 28 MCPH genes, most of which produce microtubule-associated proteins and are involved in cell division. Herein this study, 14 patients from seven Pashtun origin Pakistani families of primary microcephaly were analyzed. Mutation analysis was performed through targeted Sanger DNA sequencing on the basis of phenotype-linked genetic makeup. Genetic analysis in one family found a novel pathogenic DNA change in the abnormal spindle microtubule assembly (ASPM) gene (NM_018136.4:c.3871dupGA), while the rest of the families revealed recurrent nonsense mutation c.3978G>A (p.Trp1326*) in the same gene. The novel reported frameshift insertion presumably truncates the protein p.(Lys1291Glyfs*14) and deletes the N-terminus domains. Identification of novel ASPM-truncating mutation expands the mutational spectrum of the ASPM gene, while mapping of recurrent mutation c.3978G>A (p.Trp1326*) will aid in establishing its founder effect in the Khyber Pakhtunkhwa (KPK) inhabitant population of Pakistan and should be suggestively screened for premarital counseling of MCPH susceptible families. Most of the recruited families are related to first-degree consanguinity. Hence, all the family elders were counseled to avoid intrafamilial marriages.

Public-Private Mix Models of Tuberculosis Care in Pakistan: A High-Burden Country Perspective.

Introduction: Pakistan ranks fifth in the globally estimated burden of tuberculosis (TB) case incidence. Annually, a gap of 241,688 patients with TB exists between estimated TB incidence and actual TB case notification in Pakistan. These undetected/missed TB cases initiate TB care from providers in the private healthcare system who are less motivated to notify patients to the national database that leads to significant underdetection of actual TB cases in the Pakistani community. To engage these private providers in reaching out to missing TB cases, a national implementation trial of the Public-Private Mix (PPM) model was cohesively launched by National TB Control Program (NTP) Pakistan in 2014. The study aims to assess the implementation, contribution, and relative treatment outcomes of cohesively implemented PPM model in comparison to the non-PPM model. Methods: A retrospective record review of all forms (new and relapse) patients with TB notified from July 2015 to June 2016 was conducted both for PPM- and non-PPM models. Results: The PPM model was implemented in 92 districts in total through four different approaches and contributed 25% (81,016 TB cases) to the national TB case notification. The PPM and non-PPM case notification showed a strong statistical difference in proportions among compared variables related to gender (p < 0.001), age group (p < 0.000), and province (p < 0.000). Among PPM approaches, general practitioners and non-governmental-organization facilities achieve a treatment success of 94-95%; private hospitals achieve 82% success, whereas Parastatals are unable to follow more than half of their notified TB cases. Discussion: The PPM model findings in Pakistan are considerably consistent with countries that have prioritized PPM for an increasing trend in the TB case notification to their national TB control programs. Different PPM approaches need to be scaled up in terms of PPM implemented districts, PPM coverage, PPM coverage efficiency, and PPM coverage outcome in the Pakistani healthcare system in the future.

An Efficient Anomaly Recognition Framework Using an Attention Residual LSTM in Surveillance Videos.

Video anomaly recognition in smart cities is an important computer vision task that plays a vital role in smart surveillance and public safety but is challenging due to its diverse, complex, and infrequent occurrence in real-time surveillance environments. Various deep learning models use significant amounts of training data without generalization abilities and with huge time complexity. To overcome these problems, in the current work, we present an efficient light-weight convolutional neural network (CNN)-based anomaly recognition framework that is functional in a surveillance environment with reduced time complexity. We extract spatial CNN features from a series of video frames and feed them to the proposed residual attention-based long short-term memory (LSTM) network, which can precisely recognize anomalous activity in surveillance videos. The representative CNN features with the residual blocks concept in LSTM for sequence learning prove to be effective for anomaly detection and recognition, validating our model's effective usage in smart cities video surveillance. Extensive experiments on the real-world benchmark UCF-Crime dataset validate the effectiveness of the proposed model within complex surveillance environments and demonstrate that our proposed model outperforms state-of-the-art models with a 1.77%, 0.76%, and 8.62% increase in accuracy on the UCF-Crime, UMN and Avenue datasets, respectively.

Engaging Community Pharmacies in Early Detection of Missing Tuberculosis Patients through Public-Private Mix Intervention in Pakistan.

Globally, Pakistan ranks fifth in terms of missing tuberculosis (TB) patients' burden. Missed TB cases are either undiagnosed or diagnosed but not notified to the national TB database. Public-private mix interventions are contributing significantly to the case detection, diagnosis, and treatment of TB in Pakistan. However, it is estimated that many cases of infected TB patients go undetected. It is likely that these "undiagnosed" active TB cases seek treatment from community pharmacies, among other venues. This study aimed at assessing the feasibility of community pharmacy-based TB case detection. Case detection protocol implementation in three Pakistani districts in a nonrandom selection of pharmacies was followed by a review of routinely maintained prospective records of patients referred from these private community pharmacies to general practitioner (GP) clinics. The study engaged 500 community pharmacies for referring presumptive TB patients to GP clinics. In total, 85% of the engaged pharmacies remained active in providing referrals during the study period. The community pharmacy-referral network achieved an annual referral rate of 3,025 presumptive TB patients and identified 547 active TB cases for the period January-December 2017. Every fifth referral among presumptives presenting and counseled at pharmacies was diagnosed with TB at GP clinics. This contribution was 9% of all new TB cases identified in these districts through all other private venues linked with the Greenstar Social Marketing setup. Identified barriers and facilitators to implementation and cost effectiveness of pharmacy models for TB case detection should be considered if the model were to be scaled up.