FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3570202> ?p ?o ?g. }

• W3570202 endingPage "62" @default.
• W3570202 startingPage "56" @default.
• W3570202 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<Φ<1 and 0<ρ<1 denote respectively the probability for a reason to exist in a patient and the probability for a patient to be non-adherent to the prescribed medicines. Of interest to the medical community is the trend of the sum, T = X+Y and Z = n-X-Y denoting respectively the total number of non-adherent and adherent patients irrespective of a reason. Hence, this article constructs a bivariate probability distribution for T and Z utilize it to explain several non-trivialities. To illustrate, non-adherence patients’ data in the literature are considered. Because the bivariate probability distribution is not seen in the literature, it is named as non-adherent bivariate distribution. Various statistical properties of the non-adherent bivariate distribution are identified and explained. An information based hypothesis testing procedure is devised to check whether an estimate of the parameter, ρ is significant. Two closely connected factors for the patients not adhering to the prescribed medicines are examined. The first is a precursor and it is that too many medicines are prescribed to take. In an illustration for the first reason, the probability for a patient not to adhere the medicines is estimated to be 0.78 which is statistically significant. The second is the post cursor and it is that the patients not-adhering to the medicines are more often hospitalized again. In an illustration of the second factor, the probability for the diabetic patients not to adhere the medicines is estimated to be 0.44 which is significant. The statistical power of accepting the true non-adherence probability by our methodology is excellent in both illustrations. A few comments are made about the future research work. Other reasons for the patients’ non-adherence might exist and they should also be examined. A regression type prediction model can be constructed if additional data on covariates are available. A principal component analysis might reveal clusters of reasons along with the grouping of illnesses if such multivariate data become available. The usual principal component analysis requires bivariate normally distributed data. For the data governed by the non-adherent bivariate distribution, a new principal component methodology needs to be devised and it will be done in a future research article. The contents of this article is the conceptual foundation for such future research work." @default.
• W3570202 created "2016-06-24" @default.
• W3570202 creator A5084561111 @default.
• W3570202 date "2014-02-01" @default.
• W3570202 modified "2023-10-18" @default.
• W3570202 title "PROBING NON-ADHERENCE TO PRESCRIBED MEDICINES? A BIVARIATE DISTRIBUTION WITH INFORMATION NUCLEUS CLARIFIES" @default.
• W3570202 cites W1246084508 @default.
• W3570202 cites W1965555277 @default.
• W3570202 cites W1981184670 @default.
• W3570202 cites W1984207232 @default.
• W3570202 cites W1995875735 @default.
• W3570202 cites W2022261649 @default.
• W3570202 cites W2053565514 @default.
• W3570202 cites W2066722558 @default.
• W3570202 cites W2075320634 @default.
• W3570202 cites W2313039720 @default.
• W3570202 cites W56793145 @default.
• W3570202 doi "https://doi.org/10.3844/amjsp.2014.56.62" @default.
• W3570202 hasPublicationYear "2014" @default.
• W3570202 type Work @default.
• W3570202 sameAs 3570202 @default.
• W3570202 citedByCount "1" @default.
• W3570202 countsByYear W35702022017 @default.
• W3570202 crossrefType "journal-article" @default.
• W3570202 hasAuthorship W3570202A5084561111 @default.
• W3570202 hasBestOaLocation W35702021 @default.
• W3570202 hasConcept C105795698 @default.
• W3570202 hasConcept C118552586 @default.
• W3570202 hasConcept C129848803 @default.
• W3570202 hasConcept C136764020 @default.
• W3570202 hasConcept C144133560 @default.
• W3570202 hasConcept C145097563 @default.
• W3570202 hasConcept C162118730 @default.
• W3570202 hasConcept C169900460 @default.
• W3570202 hasConcept C185592680 @default.
• W3570202 hasConcept C198531522 @default.
• W3570202 hasConcept C33923547 @default.
• W3570202 hasConcept C41008148 @default.
• W3570202 hasConcept C43617362 @default.
• W3570202 hasConcept C512399662 @default.
• W3570202 hasConcept C64341305 @default.
• W3570202 hasConcept C71924100 @default.
• W3570202 hasConceptScore W3570202C105795698 @default.
• W3570202 hasConceptScore W3570202C118552586 @default.
• W3570202 hasConceptScore W3570202C129848803 @default.
• W3570202 hasConceptScore W3570202C136764020 @default.
• W3570202 hasConceptScore W3570202C144133560 @default.
• W3570202 hasConceptScore W3570202C145097563 @default.
• W3570202 hasConceptScore W3570202C162118730 @default.
• W3570202 hasConceptScore W3570202C169900460 @default.
• W3570202 hasConceptScore W3570202C185592680 @default.
• W3570202 hasConceptScore W3570202C198531522 @default.
• W3570202 hasConceptScore W3570202C33923547 @default.
• W3570202 hasConceptScore W3570202C41008148 @default.
• W3570202 hasConceptScore W3570202C43617362 @default.
• W3570202 hasConceptScore W3570202C512399662 @default.
• W3570202 hasConceptScore W3570202C64341305 @default.
• W3570202 hasConceptScore W3570202C71924100 @default.
• W3570202 hasIssue "2" @default.
• W3570202 hasLocation W35702021 @default.
• W3570202 hasOpenAccess W3570202 @default.
• W3570202 hasPrimaryLocation W35702021 @default.
• W3570202 hasRelatedWork W1771779360 @default.
• W3570202 hasRelatedWork W2103329323 @default.
• W3570202 hasRelatedWork W2119095362 @default.
• W3570202 hasRelatedWork W2172032168 @default.
• W3570202 hasRelatedWork W2290081135 @default.
• W3570202 hasRelatedWork W2701784279 @default.
• W3570202 hasRelatedWork W3022782703 @default.
• W3570202 hasRelatedWork W4226066015 @default.
• W3570202 hasRelatedWork W56086665 @default.
• W3570202 hasRelatedWork W871906761 @default.
• W3570202 hasVolume "5" @default.
• W3570202 isParatext "false" @default.
• W3570202 isRetracted "false" @default.
• W3570202 magId "3570202" @default.
• W3570202 workType "article" @default.