Socioeconomic inequality in child injury in Bangladesh – implication for developing countries

<p>Abstract</p> <p>Background</p> <p>Child injury is an emerging public health issue in both developed and developing countries. It is the main cause of deaths and disabilities of children after infancy. The aim of this study was to investigate the socioeconomic inequality in injury related morbidity and mortality among 1–4 years children.</p> <p>Materials and methods</p> <p>Data used for this study derived from Bangladesh Health and Injury Survey. A multistage cluster sampling technique was conducted for this survey. In this study quintiles of socioeconomic status were calculated on the basis of assets and wealth score by using principle component analysis. The numerical measures of inequality in mortality and morbidity were assessed by the concentration index.</p> <p>Results</p> <p>The poorest-richest quintile ratio of mortality due to injury was 6.0 whereas this ratio was 5.6 and 5.5 for the infectious diseases and non-communicable diseases. The values of mortality concentration indices for child mortality due to infection, non-communicable diseases and injury causes were -0.40, -0.32 and -0.26 respectively. Among the morbidity concentration indices, injury showed significantly greater inequality. All the concentration indices revealed that there were significant inequalities among the groups. The logistic regression analysis indicated that poor children were 2.8 times more likelihood to suffer from injury mortality than rich children, taking into account all the other factors.</p> <p>Conclusion</p> <p>Despite concentration indices used in this study, the analysis reflected the family's socioeconomic position in a Bangladesh context, showing a very strong statistical association with child mortality. Due to the existing socioeconomic situation in Bangladesh, the poor children were more vulnerable to injury occurrence.</p

Effect of an Enhanced Self-Care Protocol on Lymphedema Status among People affected by Moderate to Severe Lower-Limb Lymphedema in Bangladesh, a Cluster Randomized Controlled Trial

Background: Lymphatic filariasis (LF) is a major cause of lymphedema, affecting over 16 million people globally. A daily, hygiene-centered self-care protocol is recommended and effective in reducing acute attacks caused by secondary infections. It may also reverse lymphedema status in early stages, but less so as lymphedema advances. Lymphatic stimulating activities such as self-massage and deep-breathing have proven beneficial for cancer-related lymphedema, but have not been tested in LF-settings. Therefore, an enhanced self-care protocol was trialed among people affected by moderate to severe LF-related lymphedema in northern Bangladesh. Methods: Cluster randomization was used to allocate participants to either standard- or enhanced-self-care groups. Lymphedema status was determined by lymphedema stage, mid-calf circumference, and mid-calf tissue compressibility. Results: There were 71 patients in each group and at 24 weeks, both groups had experienced significant improvement in lymphedema status and reduction in acute attacks. There was a significant and clinically relevant between-group difference in mid-calf tissue compressibility with the biggest change observed on legs affected by severe lymphedema in the enhanced self-care group (∆ 21.5%, −0.68 (−0.91, −0.45), p < 0.001). Conclusion: This study offers the first evidence for including lymphatic stimulating activities in recommended self-care for people affected by moderate and severe LF-related lymphedem

Insights on Lymphedema Self-Care Knowledge and Practice in Filariasis and Podoconiosis-Endemic Communities in Bangladesh and Ethiopia

Lymphedema is a life-long sequelae to several neglected tropical diseases (NTD). In Bangladesh the main cause is lymphatic filariasis (LF) and Ethiopia is endemic for both LF and podoconiosis. The World Health Organization (WHO) recommends daily self-care including meticulous washing and drying of affected skin and attention to entry lesions, limb exercises and elevation. Adherence to this regime reduces secondary infections which cause disabling episodes of acute dermato-lymphangitis (ADL). Self-care practices must be integrated into family life, supported by community and monitored by health staff; however, little is known about the influence of personal and socio-demographic factors on adherence. People affected by lymphedema (n=272), adult caregivers (n=272), and health workers (n=68) in Bangladesh and Ethiopia were trained in lymphedema management according to WHO recommendations. Surveys on the causes and management of lymphedema were collected at baseline and 24-weeks, and patients completed a daily journal of self-care activities and symptoms. At baseline knowledge on causes and management of lymphedema was greater among health workers (&amp;gt;70%) than patients and caregivers (&amp;lt;20%) in both countries, and there were significant between-country differences in patient reported use of limb washing (Bangladesh = 7.7%. Ethiopia = 51.1%, p = 0.001). At 24-weeks knowledge on lymphedema causes and management had increased significantly among patients and caregivers, there was &amp;lt;70% adherence to limb washing and exercises, but lesser use of limb elevation in both countries. A range of patient characteristics were associated with significant variation in self-care, except for limb washing. Performance of fewer leg exercises was significantly associated with increased age or severe lymphedema in Bangladesh, and with being female or in paid work in Ethiopia. Patient journals recorded ADL symptoms and working days lost due to lymphedema more frequently than were reported by recall during the 24-week survey. Core elements of lymphedema self-care education, training and monitoring are the same for multiple etiologies. This creates opportunities for cross-cutting implementation of integrated service delivery across several skin NTDs. Sustainability will depend on community level ownership and research on factors affecting adherence to lymphedema self-care are urgently needed.</jats:p

Impact of opioid-free analgesia on pain severity and patient satisfaction after discharge from surgery: multispecialty, prospective cohort study in 25 countries

Background: Balancing opioid stewardship and the need for adequate analgesia following discharge after surgery is challenging. This study aimed to compare the outcomes for patients discharged with opioid versus opioid-free analgesia after common surgical procedures.Methods: This international, multicentre, prospective cohort study collected data from patients undergoing common acute and elective general surgical, urological, gynaecological, and orthopaedic procedures. The primary outcomes were patient-reported time in severe pain measured on a numerical analogue scale from 0 to 100% and patient-reported satisfaction with pain relief during the first week following discharge. Data were collected by in-hospital chart review and patient telephone interview 1 week after discharge.Results: The study recruited 4273 patients from 144 centres in 25 countries; 1311 patients (30.7%) were prescribed opioid analgesia at discharge. Patients reported being in severe pain for 10 (i.q.r. 1-30)% of the first week after discharge and rated satisfaction with analgesia as 90 (i.q.r. 80-100) of 100. After adjustment for confounders, opioid analgesia on discharge was independently associated with increased pain severity (risk ratio 1.52, 95% c.i. 1.31 to 1.76; P &lt; 0.001) and re-presentation to healthcare providers owing to side-effects of medication (OR 2.38, 95% c.i. 1.36 to 4.17; P = 0.004), but not with satisfaction with analgesia (beta coefficient 0.92, 95% c.i. -1.52 to 3.36; P = 0.468) compared with opioid-free analgesia. Although opioid prescribing varied greatly between high-income and low- and middle-income countries, patient-reported outcomes did not.Conclusion: Opioid analgesia prescription on surgical discharge is associated with a higher risk of re-presentation owing to side-effects of medication and increased patient-reported pain, but not with changes in patient-reported satisfaction. Opioid-free discharge analgesia should be adopted routinely

Harmonizing Macro-Financial Factors and Twitter Sentiment Analysis in Forecasting Stock Market Trends

The surge in generative artificial intelligence technologies, exemplified by systems such as ChatGPT, has sparked widespread interest and discourse prominently observed on social media platforms like Twitter. This paper delves into the inquiry of whether sentiment expressed in tweets discussing advancements in AI can forecast day-to-day fluctuations in stock prices of associated companies. Our investigation involves the analysis of tweets containing hashtags related to ChatGPT within the timeframe of December 2022 to March 2023. Leveraging natural language processing techniques, we extract features, including positive/negative sentiment scores, from the collected tweets. A range of classifier machine learning models, encompassing gradient boosting, decision trees and random forests, are employed to train on tweet sentiments and associated features for the prediction of stock price movements among key companies, such as Microsoft and OpenAI. These models undergo training and testing phases utilizing an empirical dataset gathered during the stipulated timeframe. Our preliminary findings reveal intriguing indications suggesting a plausible correlation between public sentiment reflected in Twitter discussions surrounding ChatGPT and generative AI and the subsequent impact on market valuation and trading activities concerning pertinent companies, gauged through stock prices. This study aims to forecast bullish or bearish trends in the stock market by leveraging sentiment analysis derived from an extensive dataset comprising 500,000 tweets. In conjunction with this sentiment analysis derived from Twitter, we incorporate control variables encompassing macroeconomic indicators, Twitter uncertainty index and stock market data for several prominent companies

Community Engagement in The Telehealth Service for Aged People with Diabetes: COVID-19 response in Bangladesh

Purpose: The purpose of this study is to present a better understanding of the specialized telehealth service in Bangladesh from the service provider and service recipients by aged people Method: Both quantitative and qualitative methods were used to collect data from Diabetes Mellitus (DM) patients. Data were collected by online telephone interviewing with an interview schedule. A total of 100 aged people with diabetes were selected purposively for a quantitative interview and 10 In-depth Interviews (IDIs) &amp; Key Informant Interviews (KIIs) were conducted. Result: The majority of patients aged was between 61 to 68 years with a mean age of 63.6 ± 7.01years. The difference of age of DM patients by sex was found statistically significant (x2 = 39.49, df = 31; Cramer’s V = .032; P=&lt;.003). The main source of information about digital health was: relatives (55%), neighbors (31%), television (12%), newspaper (10%), social media (9%), and healthcare providers (6%). Strong relationship was found between age of respondents and sources of information (x2= 77.08; Cramer’s V= .032, df = 13; Sig; P= &lt; .009). About 59% of DM patients were benefited from telehealth services during COVID-19, however; they encountered some difficulties like effective access to digital technology, cost, and diagnosis facilities. About 83% of respondents suggest formalizing community engagement programs to extend the digital health services during a health emergency. The common barriers to the engagement of community people in digital health care are lack of social awareness, lack of peer group support, and gender disparities. Poor counseling, language barrier, bad internet signal, and lack of family members' support were the key barriers during teleconsultation services. Conclusion: Telehealth has the potential to address critical health issues of aged people and effective community engagement may be the best option to reach older people with diabetes in Bangladesh during any health emergency

A Comprehensive Exploration of Outlier Detection in Unstructured Data for Enhanced Business Intelligence Using Machine Learning

Due to the rapid growth of online data, it is evident that social informatics faces a significant obstacle. The task of effectively utilizing this abundance of information for business intelligence purposes and extracting valuable insights from it across diverse and heterogeneous platforms presents a daunting challenge. Coordinating AI with business knowledge stands apart as an essential worry in the ongoing scene. Customarily, exceptions were many times excused as boisterous information, bringing about the deficiency of relevant data. This paper highlights the need to rethink how outliers are handled and shed light on the primary research challenges in this mining subfield. It presents a thorough scientific categorization of different Business Knowledge strategies and diagrams their ongoing application areas. Also, the paper talks about future exploration bearings and proposals to overcome any barrier concerning oddities in information examination, consequently empowering more successful business methodologies. This work plans to improve the usage of tremendous web-based information hotspots for better business insight results