scholarly journals Prevalence, clinical relevance and predictive factors of medication discrepancies revealed by medication reconciliation at hospital admission: prospective study in a Swiss internal medicine ward

BMJ Open ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. e026259 ◽  
Author(s):  
Olivier Giannini ◽  
Nicole Rizza ◽  
Michela Pironi ◽  
Saida Parlato ◽  
Brigitte Waldispühl Suter ◽  
...  
Keyword(s):  
Internal Medicine ◽  
Regression Model ◽  
Poisson Regression ◽  
Medication Reconciliation ◽  
Secondary Outcome ◽  
Poisson Regression Model ◽  
Internal Medicine Ward ◽  
Medication History ◽  
Medication Discrepancies ◽  
Standard Medication

ObjectiveMedication reconciliation (MedRec) is a relevant safety procedure in medication management at transitions of care. The aim of this study was to evaluate the impact of MedRec, including abest possible medication history(BPMH) compared with a standard medication history in patients admitted to an internal medicine ward.DesignProspective interventional study. Data were analysed using descriptive statistics followed by univariate and multivariate Poisson regression models and a zero-inflated Poisson regression model.SettingInternal medicine ward in a secondary care hospital in Southern Switzerland.ParticipantsThe first 100 consecutive patients admitted in an internal medicine ward.Primary and secondary outcome measuresMedication discrepancies between the medication list obtained by the physician and that obtained by a pharmacist according to a systematic approach (BPMH) were collected, quantified and assessed by an expert panel that assigned a severity score. The same procedure was applied to discrepancies regarding allergies. Predicting factors for medication discrepancies were identified.ResultsThe median of medications per patient was 8 after standard medication history and 11 after BPMH. Total admission discrepancies were 524 (5.24 discrepancies per patient) with at least 1 discrepancy per patient. For 47 patients, at least one discrepancy was classified as clinically relevant. Discrepancies were classified as significant and serious in 19% and 2% of cases, respectively. Furthermore, 67% of the discrepancies were detected during the interview conducted by the pharmacist with the patients and/or their caregivers. The number of drugs used and the autonomous management of home therapy were associated with an increased number of clinically relevant discrepancies in a multivariable Poisson regression model.ConclusionEven in an advanced healthcare system, a standardised MedRec process including a BPMH represents an important strategy that may contribute to avoid a notable number of clinically relevant discrepancies and potential adverse drug events.

scholarly journals The effect of sociodemographic factors on COVID-19 incidence of 342 cities in China: a geographically weighted regression model analysis

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Huihui Zhang ◽  
Yini Liu ◽  
Fangyao Chen ◽  
Baibing Mi ◽  
Lingxia Zeng ◽  
...  
Keyword(s):  
Regression Model ◽  
Poisson Regression ◽  
Linear Models ◽  
Intervention Strategy ◽  
Health Resources ◽  
Sociodemographic Factors ◽  
Information Criteria ◽  
Poisson Regression Model ◽  
Geographically Weighted Regression Model ◽  
Auto Correlation

Abstract Background Since December 2019, the coronavirus disease 2019 (COVID-19) has spread quickly among the population and brought a severe global impact. However, considerable geographical disparities in the distribution of COVID-19 incidence existed among different cities. In this study, we aimed to explore the effect of sociodemographic factors on COVID-19 incidence of 342 cities in China from a geographic perspective. Methods Official surveillance data about the COVID-19 and sociodemographic information in China’s 342 cities were collected. Local geographically weighted Poisson regression (GWPR) model and traditional generalized linear models (GLM) Poisson regression model were compared for optimal analysis. Results Compared to that of the GLM Poisson regression model, a significantly lower corrected Akaike Information Criteria (AICc) was reported in the GWPR model (61953.0 in GLM vs. 43218.9 in GWPR). Spatial auto-correlation of residuals was not found in the GWPR model (global Moran’s I = − 0.005, p = 0.468), inferring the capture of the spatial auto-correlation by the GWPR model. Cities with a higher gross domestic product (GDP), limited health resources, and shorter distance to Wuhan, were at a higher risk for COVID-19. Furthermore, with the exception of some southeastern cities, as population density increased, the incidence of COVID-19 decreased. Conclusions There are potential effects of the sociodemographic factors on the COVID-19 incidence. Moreover, our findings and methodology could guide other countries by helping them understand the local transmission of COVID-19 and developing a tailored country-specific intervention strategy.

A multivariate Poisson regression model for count data

2021 ◽  
pp. 1-17
Author(s):  
J. M. Muñoz-Pichardo ◽  
R. Pino-Mejías ◽  
J. García-Heras ◽  
F. Ruiz-Muñoz ◽  
M. Luz González-Regalado
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Poisson Regression Model

Monitoring social networks based on Zero-inflated Poisson regression model

2021 ◽  
pp. 1-17
Author(s):  
Narges Motalebi ◽  
Mohammad Saleh Owlia ◽  
Amirhossein Amiri ◽  
Mohammad Saber Fallahnezhad
Keyword(s):  
Social Networks ◽  
Regression Model ◽  
Poisson Regression ◽  
Poisson Regression Model

scholarly journals Age and Sex Specific Trends in Incidence of Juvenile Idiopathic Arthritis in Danish Birth Cohorts from 1992 to 2002: A Nationwide Register Linkage Study

2021 ◽  
Vol 18 (16) ◽  
pp. 8331
Author(s):  
Isabel Cardoso ◽  
Peder Frederiksen ◽  
Ina Olmer Specht ◽  
Mina Nicole Händel ◽  
Fanney Thorsteinsdottir ◽  
...  
Keyword(s):  
Juvenile Idiopathic Arthritis ◽  
Regression Model ◽  
Incidence Rate ◽  
Poisson Regression ◽  
Linkage Study ◽  
Incidence Rates ◽  
Age Group ◽  
Poisson Regression Model ◽  
Birth Cohorts ◽  
Age And Sex

This study reports age- and sex-specific incidence rates of juvenile idiopathic arthritis (JIA) in complete Danish birth cohorts from 1992 through 2002. Data were obtained from the Danish registries. All persons born in Denmark, from 1992–2002, were followed from birth and until either the date of first diagnosis recording, death, emigration, 16th birthday or administrative censoring (17 May 2017), whichever came first. The number of incident JIA cases and its incidence rate (per 100,000 person-years) were calculated within sex and age group for each of the birth cohorts. A multiplicative Poisson regression model was used to analyze the variation in the incidence rates by age and year of birth for boys and girls separately. The overall incidence of JIA was 24.1 (23.6–24.5) per 100,000 person-years. The rate per 100,000 person-years was higher among girls (29.9 (29.2–30.7)) than among boys (18.5 (18.0–19.1)). There were no evident peaks for any age group at diagnosis for boys but for girls two small peaks appeared at ages 0–5 years and 12–15 years. This study showed that the incidence rates of JIA in Denmark were higher for girls than for boys and remained stable over the observed period for both sexes.

PARAMETER ESTIMATION ON HURDLE POISSON REGRESSION MODEL WITH CENSORED DATA

2012 ◽  
Vol 57 (1) ◽  
Author(s):  
SEYED EHSAN SAFFAR ◽  
ROBIAH ADNAN ◽  
WILLIAM GREENE
Keyword(s):  
Regression Model ◽  
Count Data ◽  
Poisson Regression ◽  
Goodness Of Fit ◽  
Poisson Model ◽  
Likelihood Method ◽  
Poisson Regression Model ◽  
Response Variable ◽  
The Mean ◽  
Over Dispersion

A Poisson model typically is assumed for count data. In many cases, there are many zeros in the dependent variable and because of these many zeros, the mean and the variance values of the dependent variable are not the same as before. In fact, the variance value of the dependent variable will be much more than the mean value of the dependent variable and this is called over–dispersion. Therefore, Poisson model is not suitable anymore for this kind of data because of too many zeros. Thus, it is suggested to use a hurdle Poisson regression model to overcome over–dispersion problem. Furthermore, the response variable in such cases is censored for some values. In this paper, a censored hurdle Poisson regression model is introduced on count data with many zeros. In this model, we consider a response variable and one or more than one explanatory variables. The estimation of regression parameters using the maximum likelihood method is discussed and the goodness–of–fit for the regression model is examined. We study the effects of right censoring on estimated parameters and their standard errors via an example.

scholarly journals A web application to involve patients in the medication reconciliation process: a user-centered usability and usefulness study

2018 ◽  
Vol 25 (11) ◽  
pp. 1488-1500
Author(s):  
Sophie Marien ◽  
Delphine Legrand ◽  
Ravi Ramdoyal ◽  
Jimmy Nsenga ◽  
Gustavo Ospina ◽  
...  
Keyword(s):  
Web Application ◽  
Medication Reconciliation ◽  
Qualitative Data ◽  
Improve Patient Safety ◽  
Three Dimensions ◽  
Medication List ◽  
Medication History ◽  
Medication Discrepancies ◽  
System Usability Scale ◽  
User Centered

Abstract Objective Medication reconciliation (MedRec) can improve patient safety by resolving medication discrepancies. Because information technology (IT) and patient engagement are promising approaches to optimizing MedRec, the SEAMPAT project aims to develop a MedRec IT platform based on two applications: the “patient app” and the “MedRec app.” This study evaluates three dimensions of the usability (efficiency, satisfaction, and effectiveness) and usefulness of the patient app. Methods We performed a four-month user-centered observational study. Quantitative and qualitative data were collected. Participants completed the system usability scale (SUS) questionnaire and a second questionnaire on usefulness. Effectiveness was assessed by measuring the completeness of the medication list generated by the patient application and its correctness (ie medication discrepancies between the patient list and the best possible medication history). Qualitative data were collected from semi-structured interviews, observations and comments, and questions raised by patients. Results Forty-two patients completed the study. Sixty-nine percent of patients considered the patient app to be acceptable (SUS Score ≥ 70) and usefulness was high. The medication list was complete for a quarter of the patients (7/28) and there was a discrepancy for 21.7% of medications (21/97). The qualitative data enabled the identification of several barriers (related to functional and non-functional aspects) to the optimization of usability and usefulness. Conclusions Our findings highlight the importance and value of user-centered usability testing of a patient application implemented in “real-world” conditions. To achieve adoption and sustained use by patients, the app should meet patients’ needs while also efficiently improving the quality of MedRec.

Using Poisson Regression Model to Examine Student Travel Frequency Patterns in Beijing

2014 ◽  
Vol 1030-1032 ◽  
pp. 2738-2741
Author(s):  
Guang Jun Zhan
Keyword(s):  
University Students ◽  
Regression Model ◽  
Poisson Regression ◽  
Family Income ◽  
Influence Factors ◽  
Poisson Regression Model ◽  
Web Based ◽  
Study Results ◽  
Online Travel ◽  
Student Travel

This paper applies Poisson regression model to examine university students' travel frequencies and relevant influence factors, using the data collected from four universities in Beijing by a web-based online travel survey. It finds that student grade, family income and school attended have significant effects on students' travel frequency. The study results reveal students travel frequency characteristics at a disaggregate level and provide information to well understand student travel frequency patterns.

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