@article {10.3844/amjsp.2014.56.62,
article_type = {journal},
title = {PROBING NON-ADHERENCE TO PRESCRIBED MEDICINES? A BIVARIATE DISTRIBUTION WITH INFORMATION NUCLEUS CLARIFIES},
author = {Shanmugam, Ramalingam},
volume = {5},
year = {2014},
month = {Jul},
pages = {56-62},
doi = {10.3844/amjsp.2014.56.62},
url = {https://thescipub.com/abstract/amjsp.2014.56.62},
abstract = {No illness gets cured without the patient’s adherence to the prescribed medicine (s). Reasons such as too many medicines, lack of health insurance coverage, high co-payment cost, loss of cognitive memory to take. are commonly noticed for non-adherence. In some illnesses, the patients who do not adhere to the prescribed medicines end up again in hospital. How should the pertinent data be analyzed to learn? Currently, there is no suitable methodology to scrutinize the data for a clear assessment about the significance of a reason. To fulfil such a need, this article develops and demonstrates a new underlying bivariate probability model for the data and a statistical methodology to extract pertinent information to check whether the non-adherent proportion of patients to medicine (s) is significant enough to come up with strict remedial policies. To start with, the case of too many prescribed medicines is examined. Then, the repeated hospitalization due to non-adherence is examined. The contents of this article could be easily extended to other reasons of non-adherence as well. In the presence of a reason, there might exist a number of non-adherent X and a number of adherent, Y patients. Both X and Y is observable in a sample of size n1 with the presence of a reason and in another random sample of size n2 with the absence of a reason. The total sample size is n = n1 + n2. Let 0},
journal = {Current Research in Medicine},
publisher = {Science Publications}
}