Browsing by Author "Kamalesh Kumar Patel"

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Bayesian competing risk analysis: An application to nasopharyngeal carcinoma patients data
(John Wiley and Sons Inc, 2021) Rakesh Kumar Saroj; K. Narasimha Murthy; Mukesh Kumar; Atanu Bhattacharjee; Kamalesh Kumar Patel
Background: The Cox proportional hazard (CPH) model is normally used to study the death event data. The presence of competing risk (CR) is often encountered in health data, hence it becomes difficult to manage time to event data in clinical study. Bayesian approach is considered to manage the CR events in clinical data. Objectives: The objective of study is to find the predictors associated with overall survival of nasopharyngeal carcinoma (NPC) patients. Further, our purpose is to use a Bayesian model that can analyze time to event data in the presence of CR. Methods: Total 245 patients with NPC were taken (https://www.ncbi.nlm.nih.gov/geo/). The sociodemographic and clinical variables were considered for analysis purposes. R software and openBUGS were used to overcome the computational problems of CPH and Bayesian models. The Markov chain Monte Carlo (MCMC) method was used to compute the regression coefficients of Bayesian model. Results: The study shows that among NPC patients, the covariates chemotherapy, smoking, N-stage, and tumor site are associated with the higher risk for the deaths occurring in the cancer patients. The posterior mean estimates of proposed Bayesian model for significant factors have been obtained. The posterior mean and standard deviation estimates help to improve the survival of patients in the presence of CR. Conclusions: It is very difficult to use the CR model with Bayesian approach in health research for nonstatistical researcher due to lack of information. This paper is dedicated to the application of Bayesian approach for CR analysis on NPC data. © 2020 The Authors. Computational and Systems Oncology published by Wiley Periodicals LLC.
Change in malnutrition among under-5 children in Mali: a comparative analysis of 2012–13 and 2018 and exploration of determinants
(Springer Nature, 2024) Kamalesh Kumar Patel; Jang Bahadur Prasad; Raghavendra Singh; Mukesh Kumar
Backgrounds: Malnutrition is a severe problem in Africa and South East Asia. Mali is one of the countries where 0–59-month-old children suffer from acute malnutrition. Hence, to monitor the health and health services, this study is aimed at identifying the change in malnutrition in under-5 children and its determinants. Materials and methods: Recent two rounds of data for Mali country were extracted from the demographic and health surveys (DHS) website (https://dhsprogram.com/data/) to study the change in nutritional status and determinants. Both round data were analyzed by using bi-variate, z-test, and binary logistic regression techniques. Results: In Mali, stunting, wasting, and underweight children were found to be 26.7%, 8.9%, and 18.5%, respectively. In addition, significant change in socio-demographic and health predictors was seen in stunting and underweight from 2012–13 to 2018. Change in the prevalence of wasting was significant in all groups of selected predictors except in the group of size and weight at birth. The education level of the mother, institutional delivery, antenatal care, mother’s anemia, tetanus injection, birth interval, mother’s body mass index (BMI), currently breastfeeding, type of residence, toilet facility, and wealth index were significantly associated with chronic malnutrition. Conclusion: Mother and social factors were the major cause of malnutrition in the country. Hence, there is a need a policy actions with a better monitoring system for improving accessibility and availability of health services at different social classes and economic levels. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2024.
Determinants of infant mortality in Pakistan: evidence from Pakistan Demographic and Health Survey 2017–18
(Springer Science and Business Media Deutschland GmbH, 2021) Kamalesh Kumar Patel; Rashmi Rai; Ambarish Kumar Rai
Background: The infant mortality rate was very high in Pakistan until the early 1990s, at 86 deaths/1000 live births. It has decreased 24 points and declined to 62 deaths/1000 in the last 3 decades, but Pakistan is still in the group of countries with highest infant mortality rate. The present study aimed to assess the magnitude of infant mortality in Pakistan and its causes and associated risk factors. Methods: For this study, data from the 2018 Pakistan Demographic and Health Survey were used. Risk factors for infant mortality were first examined in bivariate analyses. Chi-square test was employed to understand the significance level of the categorical difference of independent variables. The Cox proportional hazard model was used to account for potential confounders that function as risk factors for infant deaths. Results: Large differentials in infant survival by socioeconomic and demographic factors indicate poor coverage of social and health schemes for the public. Mothers who did not use the ANC services experienced about 1.5 times higher infant mortality than those who did (52 vs. 36/1000 LB; p = 0.007). The hazard model shows that rich households experienced about 30% (HR = 0.735; 95% CI = 0.614–0.878) less infant mortality than poor ones. The rural-urban differential in public health services and gender inequities are the underlying causes of the stagnation of infant mortality in Pakistan. Conclusion: The low status of women’s education, poor economic conditions and low level of using public health care services are closely tied to higher infant death rates in Pakistan. Health interventions in Pakistan should be designed to reach the most under-served—women and children—especially in rural areas. © 2020, Springer-Verlag GmbH Germany, part of Springer Nature.
Sequential testing procedure for the parameter of left truncated exponential distribution
(Natural Sciences Publishing, 2020) Mukesh Kumar; Aanchal Anant Awasthi; Ajay Kumar; Kamalesh Kumar Patel
In this paper, we have explored the sequential testing procedure for the left truncated exponential distribution (LTED). Also we have obtained the expressions of operating characteristic (OC) and average sample number (ASN) functions for left truncated exponential distribution.These results are presented through tables and graphs. The sequential probability ratio test (SPRT) is used for testing the hypothesis regarding parameter. © 2020 NSP.
When COVID-19 will decline in India? Prediction by combination of recovery and case load rate
(Elsevier B.V., 2021) Atanu Bhattacharjee; Mukesh Kumar; Kamalesh Kumar Patel
Background: The World Health Organization (WHO) declared COVID-19 as a pandemic on March 11, 2020. There is sudden need of statistical modeling due to onset of COVID-19 pandemic across the world. But health planning and policy requirements need the estimates of disease problem from clinical data. Objective: The present study aimed to predict the declination of COVID-19 using recovery rate and case load rate on basis of available data from India. Methods: The reported COVID-19 cases in the country were obtained from website (https://datahub.io/core/covid-19#resource-covid-19_zip/). The confirmed cases, recovered cases and deaths were used for estimating recovery rate, case load rate and death rate till June 04, 2020. Results: A total of 216919 confirmed cases were reported nationwide in India on June 04, 2020. It is found that the recovery rate increased to 47.99% and case load rate decreased to 49.21%. Death rate is found to be very low 2.80%. Accordingly, coincidence of the difference of case load rate and recovery rate (delta) will reveal a declination in expected COVID-19 cases. Conclusion: The epidemic in the country was mainly caused by the movement of people from various foreign countries to India. Lockdown as restricting the migration of population and decision taken by the government to quarantine the population may greatly reduce the risk of continued spread of the epidemic in India. This study predicts that when the case load rate gets lesser than recovery rate, there after COVID-19 patients would be started to decline. © 2020