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Item Achieving the One Health Goal: Highlighting Groundwater Quality and Public Health(Water, 2022) Peiyue Li; Jianhua Wu; Saurabh ShuklaIn many regions of the world, groundwater is the main water source for multiple uses, including for drinking, irrigation, and industry. Groundwater quality, therefore, is closely related to human health, and the consumption of contaminated groundwater can induce various waterborne diseases. In the last ten years, the world has witnessed a rapid development in groundwater quality research and the assessment of associated health risks. This editorial introduced the foundation of the current Special Issue, Groundwater Quality and Public Health, briefly reviewed recent research advances in groundwater quality and public health research, summarized the main contribution of each published paper, and proposed future research directions that researchers should take into account to achieve the one health goal. It is suggested that groundwater quality protection should be further emphasized to achieve the one health goal and the UN's SDGs. Modern technologies should be continuously developed to remediate and control groundwater pollution, which is a major constrain in the development of a sustainable society.Item A Framework of Intelligent Mental Health Monitoring in Smart Cities and Societies(IETE J. Res., 2023) Arpita Chakraborty; Jyoti Sekhar Banerjee; Ritam Bhadra; Anik Dutta; Shatabdi Ganguly; Deblina Das; Souvik Kundu; Mufti Mahmud; Gautam SahaIn any smart city and society, the citizens' mental health is one of the utmost concerns. Nowadays, people from different sectors of our community face a severe mental health threat due to the prolonged pandemic of COVID-19. Depression, anxiety, suicidal behaviours, and posttraumatic stress disorder are widespread terms nowadays for students, health care workers, jobless people, etc. And Machine Learning (ML), image processing, expert systems, Internet of Things (IoT) are performing an essential function in the significant acceleration of the automation process within the healthcare industry. Therefore, this article aims to address the problem of preventing mental health disorders by early predicting individuals using the developed web portal Mind Turner; and by integrating the mentioned emerging tools in this way, later chronic mental health disorders can be avoided. We used the Random Forest Classifier to detect stress levels from the Question-Answer-based assessment, and SVM is used to detect facial emotions. Finally, both are combined using Interval Type-2 Fuzzy Logic to predict the probable mental health of a person, i.e. acute depression, moderate depression and not depressed.Item Addressing data and methodological limitations in estimating catastrophic health spending and impoverishment in India, 2004-18(Int. J. Equity Health, 2021) Sanjay K. Mohanty; Laxmi Kant DwivediBackgroundEstimates of catastrophic health expenditure (CHE) are counterintuitive to researchers, policy makers, and developmental partners due to data and methodological limitation. While inferences drawn from use of capacity-to-pay (CTP) and budget share (BS) approaches are inconsistent, the non-availability of data on food expenditure in the health survey in India is an added limitation.MethodsUsing data from the health and consumption surveys of National Sample Surveys over 14years, we have overcome these limitations and estimated the incidence and intensity of CHE and impoverishment using the CTP approach.ResultsThe incidence of CHE for health services in India was 12.5% in 2004, 13.4% in 2014 and 9.1% by 2018. Among those households incurring CHE, they spent 1.25 times of their capacity to pay in 2004 (intensity of CHE), 1.71 times in 2014 and 1.31 times by 2018. The impoverishment due to health spending was 4.8% in 2004, 5.1% in 2014 and 3.3% in 2018. The state variations in incidence and intensity of CHE and incidence of impoverishment is large. The concentration index (CI) of CHE was -0.16 in 2004, -0.18 in 2014 and-0.22 in 2018 suggesting increasing inequality over time. The concentration curves based on CTP approach suggests that the CHE was concentrated among poor. The odds of incurring CHE were lowest among the richest households [OR 0.22; 95% CI: 0.21, 0.24], households with elderly members [OR 1.20; 95% CI:1.12, 1.18] and households using both inpatient and outpatient services [OR 2.80, 95% CI 2.66, 2.95]. Access to health insurance reduced the chance of CHE and impoverishment among the richest households. The pattern of impoverishment was similar to that of CHE.ConclusionIn the last 14years, the CHE and impoverishment in India has declined while inequality in CHE has increased.