Psychosocial factors of deliberate self-harm in Afghanistan: A hospital based, matched case-control study

Background: Deliberate self-harm is not only a major global public health problem but also an important index of psychological distress and a risk factor for suicide.Aims: We aimed to determine the psychosocial risk factors for deliberate self-harm in patients aged ≥ 16 years presenting to tertiary care hospitals in Kabul, Afghanistan.Methods: A matched case-control study was conducted from February 2015 to May 2015. We recruited 185 cases (patients with deliberate self-harm) and 555 age- and sex-matched controls (patients with general medical conditions) from 4 tertiary care hospitals in Kabul. We developed a questionnaire to record the sociodemographic characteristics of the participants, history of domestic violence, drug abuse and details about the act of deliberate self-harm, including the methods used. To assess depression and anxiety, we used the WHO self-reporting questionnaire (SRQ-20). Matched odds ratios with 95% confidence interval using conditional logistic regression were used to determine statistically significant associations between psychosocial factors and deliberate self-harm.Results: Family conflicts, domestic violence, interpersonal arguments and living in extended families were found to be significantly associated with deliberate self-harm.Conclusions: In Afghanistan, deliberate self-harm appears to be predominately related to interpersonal problems and family conflicts. About two-thirds of females and more than half of males scored positive for depression, yet none were receiving treatment for this. These findings have important policy implications for mental health and suicide prevention programmes in the country

Fuzzy logic based cluster head election led energy efficiency in history assisted cognitive radio networks

The performance and the network lifetime of cooperative spectrum sensing (CSS) infrastructure-based cognitive radio (CR) networks are hugely affected by the energy consumption of the power-constrained CR nodes during spectrum sensing, followed by data transmission and reception. To overcome this issue and improve the network lifetime, clustering mechanisms with several nodes inside a single cluster can be employed. It is usually the cluster head (CH) in every cluster that is responsible for aggregating the data collected from individual CR nodes before it is being forwarded to the base station (BS). In this article, an energy-efficient fuzzy logic-based clustering (EEFC) algorithm is proposed, which uses a novel set of fuzzy input parameters to elect the most suitable node as CH. Unlike most of the other probabilistic as well as fuzzy logic-based clustering algorithms, EEFC increments the fuzzy input parameters from three to four to obtain improved solutions employing the Mamdani method for fuzzification and the Centroid method for defuzzification. It ensures that the best candidate is selected for the CH role by obtaining the crisp value from the fuzzy logic rule-based system. While compared to other well-known clustering algorithms such as low-energy adaptive clustering hierarchy (LEACH), CH election using fuzzy logic (CHEF), energy-aware unequal clustering using fuzzy logic (EAUCF), and fuzzy logic-based energy-efficient clustering hierarchy (FLECH), our proposed EEFC algorithm demonstrates significantly enhanced network lifetime where the time taken for first node dead (FND) in the network is improved. Moreover, EEFC is implemented in the existing history-assisted energy efficient infrastructure CR network to analyze and demonstrate the overall augmented energy efficiency of the system

Quality of life after stroke in Pakistan

Background: There is very little information about the quality of life (QOL) of stroke survivors in LMIC countries with underdeveloped non communicable health infrastructures, who bear two thirds of the global stroke burden. Methodology: We used a sequential mix methods approach. First, a quantitative analytical cross-sectional study was conducted on 700 participants, who constituted 350 stroke survivor and their caregiver dyads. QOL of stroke survivor was assessed via Stroke Specific Quality of Life Scale (SSQOLS) whereas QOL of caregivers was assessed through RAND-36. In addition; we assessed complications, psychosocial and functional disability of stroke survivors. Following this quantitative survey, caregivers were qualitatively interviewed to uncover contextually relevant themes that would evade quantitative surveys. Multiple linear regression technique was applied to report adjusted β-coefficients with 95% C.I. Results: The QOL study was conducted from January 2014 till June 2014, in two large private and public centers. At each center, 175 dyads were interviewed to ensure representativeness. Median age of stroke survivors was 59(17) years, 68% were male, 60% reported depression and 70% suffered post-stroke complications. The mean SSQOLS score was 164.18 ± 32.30. In the final model severe functional disability [adjβ -33.77(-52.44, -15.22)], depression [adjβ- 23.74(-30.61,-16.82)], hospital admissions [adjβ-5.51(-9.23,-1.92)] and severe neurologic pain [adjβ -12.41(-20.10,-4.77)] negatively impacted QOL of stroke survivors (P \u3c 0.01). For caregivers, mean age was 39.18 ± 13.44 years, 51% were female and 34% reported high stress levels. Complementary qualitative study revealed that primary caregivers were depressed, frustrated, isolated and also disappointed by health services. Conclusion: The QOL of Stroke survivors as reported by SSQOLS score was better than compared to those reported from other LMIC settings. However, Qualitative triangulation revealed that younger caregivers felt isolated, depressed, overwhelmed and were providing care at great personal cost. There is a need to develop cost effective holistic home support interventions to improve lives of the survivor dyad as a unit

Knowledge of modifiable risk factors of heart disease among patients with acute myocardial infarction in Karachi, Pakistan: a cross sectional study

BACKGROUND: Knowledge is an important pre-requisite for implementing both primary as well as secondary preventive strategies for cardiovascular disease (CVD). There are no estimates of the level of knowledge of risk factor of heart disease in patients with CVD. We estimated the level of knowledge of modifiable risk factors and determined the factors associated with good level of knowledge among patients presenting with their first acute myocardial infarction (AMI) in a tertiary care hospital in Karachi, Pakistan. METHODS: A hospital based cross-sectional study was conducted at the National Institute of Cardiovascular Disease, a major tertiary care hospital in Karachi Pakistan. Patients admitted with their first AMI were eligible to participate. Standard questionnaire was used to interview 720 subjects. Knowledge of four modifiable risk factors of heart disease: fatty food consumption, smoking, obesity and exercise were assessed. The participants knowing three out of four risk factors were regarded as having a good level of knowledge. A multiple logistic regression model was constructed to identify the determinants of good level of knowledge. RESULTS: The mean age (SD) was 54 (11.66) years. A mere 42% of our study population had a good level of knowledge. In multiple logistic regression analysis, independent predictors of "good" level of knowledge were (odds ratio [95% confidence interval]) more than ten years of schooling were 2.5 [1.30, 4.80] (verses no schooling at all) and nuclear family system (verses extended family system) 2.54 [1.65, 3.89]. In addition, Sindhi ethnicity OR [3.03], higher level of exercise OR [2.76] and non user of tobacco OR [2.53] were also predictors of good level of knowledge. CONCLUSION: Our findings highlight the lack of good level of knowledge of modifiable risk factors for heart disease among subjects admitted with AMI in Pakistan. There is urgent need for aggressive and targeted educational strategies in the Pakistani population

High prevalence of lack of knowledge of symptoms of acute myocardial infarction inPakistan and its contribution to delayed presentationto the hospital

<p>Abstract</p> <p>Background</p> <p>We conducted an observational study to determine the delay in presentation to hospital, and its associates among patients experiencing first Acute Myocardial Infarction (AMI) in Karachi, Pakistan.</p> <p>Methods</p> <p>A hospital based cross-sectional study was conducted at National Institute of Cardiovascular Disease (NICVD) in Karachi. A structured questionnaire was used to collect data. The primary outcome was delay in presentation, defined as a time interval of six or more hours from the onset of symptoms to presentation to hospital. Logistic regression analysis was performed to determine the factors associated with prehospital delay.</p> <p>Results</p> <p>A total of 720 subjects were interviewed; 22% were females. The mean age (SD) of the subjects was 54 (± 12) years. The mean (SE) and median (IQR) time to presentation was 12.3 (1.7) hours and 3.04 (6.0) hours respectively. About 34% of the subjects presented late. Lack of knowledge of any of the symptoms of heart attack (odds ratio (95% CI)) (1.82 (1.10, 2.99)), and mild chest pain (10.05 (6.50, 15.54)) were independently associated with prehospital delay.</p> <p>Conclusion</p> <p>Over one-third of patients with AMI in Pakistan present late to the hospital. Lack of knowledge of symptoms of heart attack, and low severity of chest pain were the main predictors of prehospital delay. Strategies to reduce delayed presentation in this population must focus on education about symptoms of heart attack.</p

History-assisted energy-efficient spectrum sensing for infrastructure-based cognitive radio networks

Spectrum sensing is a prominent functionality to enable dynamic spectrum access (DSA) in cognitive radio (CR) networks. It provides protection to primary users (PUs) from interference and creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce the number of false alarms and missed detections. Continuous sensing for a long time incurs cost in terms of increased energy consumption; thus, spectrum sensing ought to be energy efficient to ensure the prolonged existence of CR devices. This paper focuses on using of history to help achieve energy-efficient spectrum sensing in infrastructure-based CR networks. The scheme employs an iteratively developed history processing database that is used by CRs to make decisions about spectrum sensing, subsequently resulting in reduced spectrum scanning and improved energy efficiency. Two conventional spectrum sensing schemes, i.e., energy detection (ED) and cyclostationary feature detection (CFD), are enriched by history to demonstrate the effectiveness of the proposed scheme. System-level simulations are performed to investigate the sensitivity of the proposed history-based scheme by performing detailed energy consumption analysis for the aforementioned schemes. Results demonstrate that the employment of history ensued in improved energy efficiency due to reduced spectrum scanning. This paper also suggests which spectrum sensing scheme can be the best candidate in a particular scenario by looking into computational complexity before comparative analysis is presented with other states of the art

On the usage of history for energy efficient spectrum sensing

Spectrum sensing is one of the most challenging issues in cognitive radio networks. It provides protection to primary users (PUs) from interference and also creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce number of false alarms and missed detection. At the same time, spectrum sensing should be energy efficient to ensure the longevity of cognitive radio devices. This work presents a novel scheme which investigates the usage of history for energy efficient spectrum sensing in infrastructure cognitive radio networks. The presented scheme employs an iteratively developed history processing database. It is shown that usage of history helps predicting PU activity and results into reduced spectrum scanning by SUs thereby improving the sensing related energy consumption

Dynamic adjustment of weighting and safety factors in playout buffers for enhancing VoIP quality

The quality of Voice over Internet Protocol (VoIP) calls is highly influenced by transmission impairments such as delay, packet loss and jitter, with jitter being manifested as one of the deleterious effects affecting its quality. A jitter buffer is usually employed at the receiver side to mitigate its effects by adapting its parameters in a trade-off between delay and packet loss. This paper proposes a novel de-jitter algorithm that adaptively changes the size of the playout buffer depending on the network states, in order to effectively handle the packet loss and delay, whereas E-model is used to quantify speech quality. Based on the statistics of the received packets, the adaptive playout buffer algorithm dynamically adjusts the weighting factor (α) and the safety factor (β) for regulating the delay and trade-off loss, thus maximizing the quality for VoIP