Framework for implementing collaborative TB-silicosis activities in India: insights from an expert panel.

Tuberculosis (TB) treatment is more challenging for patients with silicosis, as it complicates the diagnosis of both diseases and increases mortality risk. Silicosis, an incurable occupational disease, confounds the diagnosis of TB and vice versa, making it more difficult to accurately identify and treat either condition. Moreover, TB appears to accelerate the progression of silicosis. Exposure to silica dust, a common cause of silicosis, can also trigger latent TB to become active TB. This correspondence outlines a proposed framework for implementing collaborative TB-silicosis activities in India, aimed at improving early diagnosis and management for both diseases. An expert panel of medical professionals developed this framework through online consultations in October and November 2022. The panel's goal was to establish a consensus on integrating TB-silicosis activities, with a focus on early detection and proper management. The framework suggests testing all patients with silicosis for active TB and screening workers exposed to silica dust for latent TB infection. It also recommends that patients with TB who have a history of occupational exposure to silica dust should be tested for silicosis. Reliable diagnostic tools, such as chest X-rays, are emphasized, providing guidance on their use for both diseases. The proposed collaborative TB-silicosis framework offers a structured approach to identifying and managing these two diseases, contributing to the global goal of eliminating silicosis by 2030 and aligning with the World Health Organization's targets for reducing TB incidence and mortality. It recommends specific strategies for implementation, including testing, referral systems, and workplace-based interventions. The framework also underscores the need for coordinated efforts among stakeholders, including the ministries of health, labor, industry, and environment. This correspondence provides valuable insights into how India can successfully implement collaborative TB-silicosis activities, serving as a model for other regions with similar challenges.

Evaluation of Percutaneous Vertebroplasty for Management of Symptomatic Osteoporotic Compression Fracture.

INTRODUCTION: Osteoporotic vertebral compression fractures are a leading cause of disability and associated morbidities among Indian population. Worldwide, approximately 20% of elderly population above 70 years and 16% of postmenopausal women are suffering from it. Vertebral compression fractures should be aggressively treated with minimally invasive techniques such as Percutaneous Vertebroplasty (PVP) or Percutaneous Kyphoplasty (PKP) to minimize pain and disability associated with it. AIM: To evaluate the PVP in terms of pain reduction and restoration of functional abilities among the patients suffering from symptomatic osteoporotic vertebral compression fracture. MATERIALS AND METHODS: PVP using polymethyl methacrylate bone cement was performed between 2011 to 2013, on 25 patients admitted for symptomatic osteoporotic vertebral compression fracture in the Department of Orthopaedics, Institute of Post Graduate Medical Education and Research (IPGMER) and SSKM Hospital, Kolkata, West Bengal, India. All of them were followed up for one year. Pain and disability were evaluated with Visual Analogue Scale (VAS) and Oswestry Disability Questionnaire (ODQ) score respectively. Repeated measures ANOVA with Bonferroni post-hoc test was applied for significance testing. RESULTS: Reduction in pain was reported by 56% of patients within 10 minutes of operation. Mean VAS score at presentation was 8.24 (±1.16). It reduced to 6.31 (±1.21) and 2.38 (±0.08) at immediate postoperative period and after 12 months respectively. There was significant reduction (p<0.05) in pain, as measured by VAS score, started at immediate postoperative period to end of follow up period up to one year. Disability, measured by ODQ score, significantly decrease (p<0.05) over time from one week to 12-month postoperatively. At presentation, ODQ score was 93.01 (±4.54). It reduced to 76.84 (±3.76), one week after operation and 16.23 (±1.17), one year after operation. CONCLUSION: The PVP with polymethayl methacrylate bone cement is still a justified treatment procedure for osteoporotic vertebral compression fractures as it provides excellent pain relief, internal stability to the fractured vertebra thus preventing further collapse and progression of kyphosis, allowing the patients to regain normal activity at the earliest, and at a very reasonable cost with minimal complication.

A study on knowledge, attitude, and practice regarding mental health illnesses in Amdanga block, West Bengal.

BACKGROUND: The discourse of mental health is getting its due attention after all these years in India. A major threat to the mental health system is the demand side factors, namely, knowledge, attitude, and practice (KAP) of the general population toward this. In spite of growing concern regarding mental health in India, this kind of study to assess the mental health status has been very few in India, more so in West Bengal. OBJECTIVES: With this background, this study was carried out with the objectives to validate the Community Attitude to Mental Illness (CAMI), to assess the different sociodemographic factors among the study population, to assess the KAP regarding mental illness among the study population. METHODS: It was an observational, descriptive study with cross-sectional design done at Amdanga Community Development Block, North 24 Parganas, West Bengal, India, in 2015-16. Questionnaire validation to assess the KAP was the primary objective with obtaining the descriptive data were the second one. CAMI questionnaire was used which was validated for the given area by validation methods such as Cronbach's alpha and structural equation modeling. The resultant questionnaire was used in the field on adult population after a single-stage survey design to collect 730 samples. RESULTS: The test statistics showed that the questionnaire was reasonably valid after a few tweakings. SEM identified well-define domains in the attitude part. 94.9% says that they are willing to live with a people with mental illness. 14.9% has actually done so. Health-care seeking behavior shows that 19.2% will go to a GP in case of any mental illness. Furthermore, attitude toward mental illness showed mixed picture as also knowledge. This study correlated with various studies of developing countries and it was seen that these population showed markedly different attitudes for probability of the patients getting cured than many other countries. Furthermore, stigma was gradually decreasing, as evident from various other studies. CONCLUSION: This study will provide valuable insights into the cognitive and affective aspect of mental illness among these population and thus help in implementing better policies in this regard, as this is fast becoming the talk of the day.

Artificial Neural Network (ANN) Model to Predict Depression among Geriatric Population at a Slum in Kolkata, India.

INTRODUCTION: Depression is one of the most important causes of mortality and morbidity among the geriatric population. Although, the aging brain is more vulnerable to depression, it cannot be considered as physiological and an inevitable part of ageing. Various sociodemographic and morbidity factors are responsible for the depression among them. Using Artificial Neural Network (ANN) model depression can be predicted from various sociodemographic variables and co morbid conditions even at community level by the grass root level health care workers. AIM: To predict depression among geriatric population from sociodemographic and morbidity attributes using ANN. MATERIALS AND METHODS: An observational descriptive study with cross-sectional design was carried out at a slum under the service area of Bagbazar Urban Health and Training Centre (UHTC) in Kolkata. Among 126 elderlies under Bagbazar UHTC, 105 were interviewed using predesigned and pretested schedule. Depression status was assessed using 30 item Geriatric Depression Scale. WEKA 3.8.0 was used to develop the ANN model and test its performance. RESULTS: Prevalence of depression among the study population was 45.7%. Various sociodemographic variables like age, gender, literacy, living spouse, working status, personal income, family type, substance abuse and co morbid conditions like visual problem, mobility problem, hearing problem and sleeping problem were taken into consideration to develop the model. Prediction accuracy of this ANN model was 97.2%. CONCLUSION: Depression among geriatric population can be predicted accurately using ANN model from sociodemographic and morbidity attributes.

AUDIT (Alcohol Use Disorders Identification Test) to Estimate the Pattern and Correlates of Alcohol Consumption among the Adult Population of West Bengal, India: A Community Based Cross-sectional Study.

INTRODUCTION: Pattern of alcohol consumption substantially changed in India with in last 20 to 25 years. Excessive alcohol consumption is a major risk factor for various morbidity and mortality. So, scientific study to identify alcohol consumption patterns and its correlates will be helpful to formulate preventive strategies. AIM: To estimate the pattern of alcohol consumption and to determine its correlates, among the adult population of the state of West Bengal in India. MATERIALS AND METHODS: A community based cross-sectional study was conducted among the adult population of the state of West Bengal at Gram Panchayat (GP) level. Ninety-nine (n=99) adult (≥ 18 years) men and women of Guchati GP at Paschim Medinipur district, was selected using Lot Quality Assurance Sampling (LQAS) technique. The study participants were interviewed using a pre-designed and pre-tested schedule. RESULTS: It was estimated that the prevalence of low risk drinking or abstinence (Zone I) was 65.5% (95% CI 55.5% to 75.5%) and the prevalence of alcohol use in excess of low-risk (Zone II) was 17.6% (95% CI 7.6% to 27.6%), and the prevalence of harmful and hazardous drinking (Zone III) was 8.5% (95% CI 0% to 18.5%) and the prevalence alcohol dependence (Zone-IV) was 8.4% (0% to 18.4%). Logistic regression model shows that "gender" (p = 0.00) and "employment status" (p = 0.01) added significantly to the model with adjusted odds ratio of 82.27 (95% CI 18.17-372.58) and 0.13 (0.03-0.66). CONCLUSION: There is a need for comprehensive screening and treatment programme to deal with the problems of Alcohol Use Disorders among adults to achieve good health and well being for sustainable development.

Design of smart neonatal health monitoring system using SMCC.

Automated health monitoring and alert system development is a demanding research area today. Most of the currently available monitoring and controlling medical devices are wired which limits freeness of working environment. Wireless sensor network (WSN) is a better alternative in such an environment. Neonatal intensive care unit is used to take care of sick and premature neonates. Hypothermia is an independent risk factor for neonatal mortality and morbidity. To prevent it an automated monitoring system is required. In this Letter, an automated neonatal health monitoring system is designed using sensor mobile cloud computing (SMCC). SMCC is based on WSN and MCC. In the authors' system temperature sensor, acceleration sensor and heart rate measurement sensor are used to monitor body temperature, acceleration due to body movement and heart rate of neonates. The sensor data are stored inside the cloud. The health person continuously monitors and accesses these data through the mobile device using an Android Application for neonatal monitoring. When an abnormal situation arises, an alert is generated in the mobile device of the health person. By alerting health professional using such an automated system, early care is provided to the affected babies and the probability of recovery is increased.

A Simulation Study on Hypothetical Ebola Virus Transmission in India Using Spatiotemporal Epidemiological Modeler (STEM): A Way towards Precision Public Health.

Background. Precision public health is a state-of-the-art concept in public health research and its application in health care. Application of information technology in field of epidemiology paves the way to its transformation to digital epidemiology. A geospatial epidemiological model was simulated to estimate the spread of Ebola virus disease after a hypothetical outbreak in India. Methods. It was a simulation study based on SEIR (Susceptible-Exposed-Infectious-Recovered) compartmental model. Simulation was done in Spatiotemporal Epidemiological Modeler (STEM). Epidemiological profile of Ebola virus, that transmitted throughout the Sierra Leon in 2014-2016, was fitted into the SEIR deterministic compartment model designed for India. Result. Spatiotemporal distribution of EVD exposed, infectious, and recovered population at 4-month interval represented by different figures. It is estimated that if no intervention is taken to stop the spread, within 2 years, almost half of the country will be effected by EVD and cumulative number of exposed individuals, infectious persons, and deaths will be 106947760, 30651674, and 18391005, respectively. Conclusion. Precision public health may play the key role to achieve the health related targets in the Sustainable Development Goals. Policy makers, public health specialists, and data scientists need to put their hands together to make precision public health a reality.