Process evaluation of a complex, multilevel, multicomponent scheme for the prevention and control of non-communicable diseases in Tamil Nadu, India: A mixed-methods protocol

Background

Non-communicable diseases (NCDs) are the leading cause of morbidity and mortality in India, necessitating development of multilevel and multicomponent interventions. Makkalai Thedi Maruthuvam (MTM) is a complex multilevel, multicomponent intervention developed and implemented by the south Indian State of Tamil Nadu. The scheme aims to deliver services for preventing and controlling diabetes, and hypertension at doorstep. This paper describes the protocol for planning and conducting the process evaluation of the MTM scheme.

Methods and analysis

The process evaluation uses mixed methods (secondary data analysis, key informant interviews, in-depth interviews, conceptual content analysis of documents, facility-based survey and non-participant observation) to evaluate the implementation of the MTM scheme. The broad evaluation questions addressed the fidelity, contexts, mechanisms of impact and challenges encountered by the scheme using the Consolidated Framework for Implementation Research (CFIR) framework. The specific evaluation questions addressed selected inputs and processes identified as critical to implementation by the stakeholders. The CFIR framework will guide the thematic analysis of the qualitative interviews to explore the adaptations and deviations introduced during implementation in various contexts. The quantitative data on the indicators developed for the specific evaluation questions will be cleaned and descriptively analysed.

Protocol for establishing a model for integrated influenza surveillance in Tamil Nadu, India

The potential for influenza viruses to cause public health emergencies is great. The World Health Organisation (WHO) in 2005 concluded that the world was unprepared to respond to an influenza pandemic. Available surveillance guidelines for pandemic influenza lack the specificity that would enable many countries to establish operational surveillance plans. A well-designed epidemiological and virological surveillance is required to strengthen a country's capacity for seasonal, novel, and pandemic influenza detection and prevention. Here, we describe the protocol to establish a novel mechanism for influenza and SARS-CoV-2 surveillance in the four identified districts of Tamil Nadu, India. This project will be carried out as an implementation research. Each district will identify one medical college and two primary health centres (PHCs) as sentinel sites for collecting severe acute respiratory infections (SARI) and influenza like illness (ILI) related information, respectively. For virological testing, 15 ILI and 10 SARI cases will be sampled and tested for influenza A, influenza B, and SARS-CoV-2 every week. Situation analysis using the WHO situation analysis tool will be done to identify the gaps and needs in the existing surveillance systems. Training for staff involved in disease surveillance will be given periodically. To enhance the reporting of ILI/SARI for sentinel surveillance, trained project staff will collect information from all ILI/SARI patients attending the sentinel sites using pre-tested tools. Using time, place, and person analysis, alerts for abnormal increases in cases will be generated and communicated to health authorities to initiate response activities. Advanced epidemiological analysis will be used to model influenza trends over time. Integrating virological and epidemiological surveillance data with advanced analysis and timely communication can enhance local preparedness for public health emergencies. Good quality surveillance data will facilitate an understanding outbreak severity and disease seasonality. Real-time data will help provide early warning signals for prevention and control of influenza and COVID-19 outbreaks. The implementation strategies found to be effective in this project can be scaled up to other parts of the country for replication and integration.

An improved approach for energy consumption minimizing in WSN using Harris hawks optimization

Wireless Sensor Network (WSN) is made up of minimal power devices (or) units spread over geographically separated locations. Sensors are grouped in the form of clusters. Every cluster has a key node known as the Cluster Head (CH). CH gathers sensed information out of its sensor nodes and transmits into a Base Station (BS). Sensors are indeed installed using non-replaceable batteries. WSN is concerned about its energy usage to reduce (or) minimize the consumption of energy as well as increase network lifetime. An improved upgraded technique is presented, which is accomplished by improving appropriate energy balancing in clusters across every sensor node in order to reduce power dissipation while networking connections. The enhanced technique was built by employing a well-known technique named cluster head selection. Accordingly, the energy consumption of WSN is reduced to prolong the network life cycle other than the network models. Furthermore, an efficient routing CH is optimized by the Average Fitness-based Harris Hawks Optimization (AF-HHO). In the WSN network, this proposed algorithm is used to locate neighbouring nodes with higher energy efficiency measurements. As a result, when compared to other conventional approaches, the simulation results demonstrate superior performance. Through the sink node, an optimal routing path for transferring data packets to neighbouring sensor nodes was discovered. The suggested technique is evaluated using energy consumption, network lifespan, and residual energy performance estimations.

Opposition based Spider Monkey Optimization Algorithm for Load Balancing in LTE Network

At Present telecommunication is progressed due to the development of Long-Term Evolution (LTE) standard. This LTE network provides high-speed wireless communication for mobile devices and satisfies requirements of customers from multi-cells. However, due to the congestion of mobile devices, each cell in the network may get overloaded. So, load balancing is the main challenge to the LTE network for reducing congestion or load in the cell. For load balancing, optimal cell selection method is presented in this paper. Initially, load factor of each cell is estimated. The load factor is compared to the pre-defined threshold load value. After comparison, the high load cell handles users to the optimal cell or low load cell. This optimal cell has been selected with the Opposition-Based Spider Monkey Optimization Algorithm (OSMOA). Simulation results show that the proposed approach works better than previous methods in terms of call blocking rate (CBR) and call Dropping Ratio (CDR).

COGNITIVE DISORDER AND DEPRESSION: (An Analysis of the Causal Relationship and Susceptibility to Relapses)

A prospective study of 45 cases of unipolar depression was conducted with the aim to examine the hypothesis that cognitive disorder predisposes to depression. The data indicated that the absence of the cognitive disorder during remission was statistically significant in 41 cases who remitted. Of the latter, 27 were free from cognitive disorder, while 14 continued with it. This fails to support the causal role of the cognitive disorder. It was also observed that those with persisting cognitive disorder in remission ran the risk of early relapse. It is suggested that persistence of cognitive disorder in remission could be a predictor of early relapse and offers the possibility of using cognitive behavioural therapy for such select cases.