Background: Syndemics or synergies of cooccurring epidemics are widely studied across health and social sciences in recent years. Methods: We conducted a meta-knowledge analysis of articles published between 2001 to 2020 in this growing field of academic scholarship. Results: We found a total of 830 articles authored by 3025 authors, mostly from high-income countries. Publications on syndemics are gradually increasing since 2003, with rapid development in 2013. Each article was cited more than 15 times on average, and most (n = 604) articles were original studies. Syndemics research focused on several areas, including HIV/AIDS, substance abuse, mental health, gender minority stressors, racism, violence, chronic physical and mental disorders, food insecurity, social determinants of health, and coronavirus disease 2019. Moreover, biopsychosocial interactions between multiple health problems were studied across medical, anthropological, public health, and other disciplines of science. Conclusions: The limited yet rapidly evolving literature on syndemics informs transdisciplinary interests to understand complex coexisting health challenges in the context of systematic exclusion and structural violence in vulnerable populations. The findings also suggest applications of syndemic theory to evaluate clinical and public health problems, examine the socioecological dynamics of factors influencing health and wellbeing, and use the insights to alleviate health inequities in the intersections of synergistic epidemics and persistent contextual challenges for population health.

Background: Syndemics or synergies of cooccurring epidemics are widely studied across health and social sciences in recent years. Methods: We conducted a meta-knowledge analysis of articles published between 2001 to 2020 in this growing field of academic scholarship. Results: We found a total of 830 articles authored by 3025 authors, mostly from high-income countries. Publications on syndemics are gradually increasing since 2003, with rapid development in 2013. Each article was cited more than 15 times on average, and most (n = 604) articles were original studies. Syndemics research focused on several areas, including HIV/AIDS, substance abuse, mental health, gender minority stressors, racism, violence, chronic physical and mental disorders, food insecurity, social determinants of health, and coronavirus disease 2019. Moreover, biopsychosocial interactions between multiple health problems were studied across medical, anthropological, public health, and other disciplines of science. Conclusions: The limited yet rapidly evolving literature on syndemics informs transdisciplinary interests to understand complex coexisting health challenges in the context of systematic exclusion and structural violence in vulnerable populations. The findings also suggest applications of syndemic theory to evaluate clinical and public health problems, examine the socioecological dynamics of factors influencing health and wellbeing, and use the insights to alleviate health inequities in the intersections of synergistic epidemics and persistent contextual challenges for population health.

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<p><strong> Objective</strong>: The purpose of this research was  to predict the tonsillitis using machine learning algorithms. Increasing utilization of smartphones with sensor systems and machine learning capability promise better M-Healthcare services. Tonsillitis is an inflammation of your tonsils.  Tonsillitis analysis requires contemporary technology.</p> <p><strong>Method:</strong> Different machine learning algorithms and frameworks used for evaluating the accuracy and performance.Artificial Neural Networks combined with picture processing and RGB color coding used for identify tonsillitis early and monitor prognosis at home. This study describes an innovative machine learning and  smartphone-based optimization approach with a linked camera. </p> <p><strong>Results:</strong>Patients in remote locations, poor and impoverished countries may check, assess, and frequently do tonsillitis exams anywhere, anytime, and any place.  This research proposes an unique method and machine learning approach to evaluate tonsillitis photos and diagnose infections with 90\% accuracy for Random Forest and Decision Tree, .</p> <p><strong>Conclusion:</strong>In this research paper, we have introduced an advanced MHealth application on human health and monitoring systems. The use and technological advancement of smartphones has skyrocketed in the last decade. Now embedded sensors in smartphone devices help to assess physiological indicators and evaluate the health status. We have demonstrated that M-health can be effectively applied in the detection of tonsillitis by using smartphone devices and machine learning</p>