Unveiling the Drivers of Regional Disparities in Under-Five Mortality in India: Insights from NFHS-4 Biometric Data

Bruce Stiler^*
*Correspondence: Bruce Stiler, Department of Biostatistics, University of Dhaka, Dhaka, Spain, Email:

Introduction

Insights from NFHS-4 Biometric Data" is a research study that aims to shed light on the factors that contribute to regional disparities in under-five mortality in India. The study utilizes data from the National Family Health Survey 4 (NFHS-4), which includes biometric information and socio-economic data, to gain deeper insights into the determinants of child mortality in different regions of the country. The study relies on the NFHS-4, which is a nationally representative survey conducted in India, providing comprehensive information on various aspects of maternal and child health, nutrition, and socio-economic status. The biometric data in NFHS-4, such as fingerprinting or anthropometric measurements, can help to improve accuracy in assessing age and health-related indicators [1].

Description

The study focuses on analysing the disparities in under-five mortality rates across different regions of India. India is a diverse country with significant regional variations in healthcare access, infrastructure, and socio-economic conditions, which can influence child health outcomes. The study likely employs advanced statistical techniques to analyse the data and assess the relationships between various factors and under-five mortality rates. Regression analysis or Biometric Data may be used to measure the strength of associations and quantify the contributions of different factors to the disparities. The research may provide valuable insights for policymakers to design targeted interventions and strategies to address the regional disparities in child mortality effectively. By understanding the specific drivers of under-five mortality in different regions, policymakers can tailor their approaches to suit the unique challenges and needs of each area. The use of biometric data in the analysis is likely to enhance the accuracy of age determination and other health-related measurements, thereby improving the quality and reliability of the findings.

By requiring multiple biometric traits for authentication, the risk of unauthorized access is significantly reduced. Implementing multimodal biometrics comes with its own set of challenges. The fusion of multiple modalities requires sophisticated algorithms and techniques to handle the complexity of combining and comparing diverse biometric data. The performance of multimodal systems depends on the quality and reliability of each individual modality, and any weaknesses in one modality may impact the overall system's effectiveness. Data interoperability and standardization can also pose challenges when integrating different biometric modalities from various vendors or systems. Furthermore, the collection and storage of multiple biometric traits raise privacy concerns, necessitating stringent data protection measures and adherence to privacy regulations. The miniaturization of sensors and advancements in lowpower wireless communication technologies have opened up new possibilities for engineers to design small and non-intrusive wearable devices that can be integrated into various sports equipment. These wearable devices offer valuable insights and data that can be used for performance monitoring, injury prevention, and enhancing overall player safety in sports [2,3].

One such application is the use of wearable devices to monitor linear and angular head accelerations in football to detect potential hazardous head impacts. These devices are typically mounted inside the football helmet, and they continuously track the frequency and severity of impacts that a player's head experiences during games or practices. This data can be crucial for identifying players at risk of head injuries and for implementing appropriate safety measures to minimize such risks. The information gathered by these devices can also contribute to the on-going research on concussions and traumatic brain injuries in sports. Similarly, in baseball and softball, wearable swing tracker devices have been developed to monitor various swing metrics. These devices are often attached to the player's bat or worn on the wrist, and they provide realtime feedback on metrics such as swing power, swing speed, and hitting zone analysis. Coaches and players can use this data to assess and improve their performance, optimize their swing mechanics, and work on specific aspects of their game. The use of machine learning and deep learning techniques is also gaining prominence, enabling systems to adapt and improve over time based on user feedback and evolving patterns. Additionally, researchers are exploring novel sensing technologies and hardware advancements to capture biometric data more accurately and efficiently. As multimodal biometrics continues to evolve, it holds the potential to revolutionize not only access control but also various other applications where reliable identity verification is essential [4,5].

Conclusion

Understanding the drivers of regional disparities in under-five mortality can have important policy implications. Policymakers can use this knowledge to design targeted interventions and policies to reduce child mortality and improve child health outcomes in regions with higher mortality rates. It can also guide resource allocation and help prioritize areas in need of more significant attention and support. In summary, the research study mentioned aims to utilize data from the NFHS-4 survey, including biometric data, to identify and understand the factors driving regional disparities in under-five mortality rates in India. The insights gained from this analysis can inform evidence-based policies and interventions to improve child health and reduce mortality disparities across different regions of the country.

Acknowledgement

We thank the anonymous reviewers for their constructive criticisms of the manuscript. The support from ROMA (Research Optimization and recovery in the Manufacturing industry), of the Research Council of Norway is highly appreciated by the authors.

Conflict of Interest

The Author declares there is no conflict of interest associated with this manuscript.

References

1. Pope, Carra. "Biometric data collection in an unprotected world: Exploring the need for federal legislation protecting biometric data." JL & Pol'y 26 (2018): 769.

Google Scholar

2. Ross, Arun, Sudipta Banerjee and Anurag Chowdhury. "Security in smart cities: A brief review of digital forensic schemes for biometric data." Pattern Recognit Lett 138 (2020): 346-354.

Google Scholar, Crossref, Indexed at

3. Sun, Yunlian, Man Zhang, Zhenan Sun and Tieniu Tan. "Demographic analysis from biometric data: Achievements, challenges, and new frontiers." IEEE (2017): 332-351.

Google Scholar, Crossref, Indexed at

4. Wright, Alexi A., Nikita Raman, Patrick Staples and Stephanie Schonholz, et al. "The hope pilot study: Harnessing patient-reported outcomes and biometric data to enhance cancer care." JCO 2 (2018): 1-12.

Google Scholar, Crossref, Indexed at

5. Kos, Marko and Iztok Kramberger. "A wearable device and system for movement and biometric data acquisition for sports applications." IEEE 5 (2017): 6411-6420.

Google Scholar, Crossref, Indexed at