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In many practical situations, complete data are not available in lifetime studies. Many of the available observations are right censored giving survival information up to a noted time and not the exact failure times. This constitutes randomly censored data. In this paper, we consider Maxwell distribution as a survival time model. The censoring time is also assumed to follow a Maxwell distribution with a different parameter. Maximum likelihood estimators and confidence intervals for the parameters are derived with randomly censored data. Bayes estimators are also developed with inverted gamma priors and generalized entropy loss function. A Monte Carlo simulation study is performed to compare the developed estimation procedures. A real data example is given at the end of the study.

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Globally, antibiotics’ consumption has been worrisome and immediate attention is required. Unused and expired antibiotics are continuously disposed of as household waste in sewage wastewater, which acts as social driver of antimicrobial resistance (AMR). A questionnaire‐based survey was conducted to identify the scale of consumption and methods of disposal used for unused/expired antibiotics. Two groups were selected based on the knowledge of usage and misuse of antibiotics, and the response was collected. Every respondent had taken at least one antibiotic, and many of them consumed without any cause that is, 72.3% in fever and 57.8% in cold. The antibiotic course completion percentage was very poor (23% and 40% for Groups 1 and 2, respectively) and ∼75% of respondents just throw the unused/expired antibiotics, while only 2–3% went for safe/other methods of disposal. Perhaps, this direct disposal of antibiotics in the environment may be one of the contributors to AMR. The present study suggests that public awareness regarding the use of prescribed antibiotics, completion of the course, and knowledge of the safe disposal of unused/expired antibiotics will be a good strategy to combat AMR.

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