EXPLORING THE PREVALENCE OF NEUROPATHIC HAND PAIN AMONG YOUNG ADULTS AND ITS IMPACT ON HAND FUNCTION

Background and Aims: To explore the prevalence of neuropathic hand pain in young adults and how it affects hand functionality. Methodology: A convenience sampling was used to conduct a cross-sectional study on 192 medical students aged 18 to 28 years from October 2022 to March 2023. The purpose of the study was to evaluate the prevalence of neuropathic hand pain in young adults and how it affects hand functionality. Both the Duruoz Hand Index and the Douleur Neuropathique Four Questionnaire were used. Results: This study included 192 participants, the majority of whom were females (84%) and aged 20-24 years. 16.1% of participants reported having had surgery or an injury to their upper limb in the past. Participants described neuropathic pain symptoms such as electric shocks (21.4%), burning pain (18.2%), and painful cold (15.6%). Limitations and Future Implications: A small sample size and a focus on a particular group of medical students may restrict the generalizability of the findings. To improve the generalizability of the findings, future research should replicate these findings using larger sample sizes and diverse populations. Insights into the development and chronicity of neuropathic hand pain in young adults may also come from longitudinal studies. Conclusion: Our study demonstrates the prevalence of neuropathic hand pain in young adults and how it affects hand functionality. Female participants were more likely to experience pain and functional hand impairments.

Generalized Mean Estimators for Sensitive and Non-Sensitive Variables in the Presence of Measurement Errors.

In survey research, accurate collection and recording of information is very critical. The researcher must deal with many potential problems. First the response rate may be poor due to various reasons such as poorly prepared survey instrument, poor execution of survey, survey questions being very personal in nature, and untrained field workers. Some of these issues lead to measurement errors which are the most common form of non-sampling errors. More formally, these errors are defined as the difference between the true value of a variable and its recorded value. It is for this reason these errors are also known as observational errors. Measurement errors have been studied by various authors with Cochran (1963) drawing early attention to these errors. While mean estimation for nonsensitive variables has been studied extensively in the presence of measurement errors, no attempt has been made to study mean estimation for sensitive variables in the presence of measurement errors. By sensitive variable, we mean a variable for which there is a natural tendency on the part of survey respondent to either refuse to answer or to give a socially desirable answer as opposed to correct answer. Randomized Response Technique (RRT) introduced originally by Warner (1965), and later refined by many researchers, is a great tool to deal with the problem of social desirability bias in surveys involving sensitive questions. The main focus of this thesis is on introducing a generalized mean estimator for non-sensitive as well as sensitive quantitative variables in the presence of measurement errors, and on studying the impact of measurement errors on mean estimation. In Chapter 1, we have provided a brief discussion about measurement errors, sensitive variables, and various versions of the Randomized Response Techniques (RRT). Furthermore, measurement errors under simple random sampling and stratified random sampling have been illustrated. Greater details on these two important topics, measurement errors and randomized response methodology, are provided as part of literature review in Chapter 2. In Chapter 3, we have reviewed some existing mean estimators for non-sensitive and sensitive study variables in the presence of measurement errors under both sampling designs. The major contributions of this thesis start from Chapter 4. In this chapter, a generalized mean estimator for a non-sensitive study variable under simple random sampling design has been proposed to examine the impact of measurement errors on mean estimation. Some special cases for generalized mean estimator have also been discussed. In Chapter 5, we continue the study undertaken in Chapter 4 but in the context of the stratified random sampling design. Chapters 6 and 7 are very important chapters where we have examined the impact of measurement errors on mean estimation of a sensitive study variable under the simple random sampling design and the stratified random sampling design respectively. We have used extensive simulations and numerical examples to validate our theoretical findings. Finally some concluding remarks with some possible future directions are mentioned in Chapter 8.