scholarly journals Impacts of multimorbidity on medication treatment, primary healthcare and hospitalization among middle-aged and older adults in China: evidence from a nationwide longitudinal study

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yang Zhao ◽  
Siqi Zhao ◽  
Lin Zhang ◽  
Tilahun Nigatu Haregu ◽  
Haipeng Wang
Keyword(s):  
Logistic Regression ◽  
Longitudinal Study ◽  
Linear Regression ◽  
Random Effects ◽  
Primary Healthcare ◽  
Outpatient Care ◽  
Regression Models ◽  
Financial Burden ◽  
Linear Regression Models ◽  
Log Linear

Abstract Background Multimorbidity is a significant contributor to inequalities in healthcare and has become a major unaddressed challenge for the health system in China. The aim of this study is to assess the socio-demographic distribution of multimorbidity and the relationships between multimorbidity, primary healthcare, hospitalization and healthcare spending. Methods We conducted this nationwide population-based panel data study in China. Study participants included 12,306 residents aged ≥45 years from the China Health and Retirement Longitudinal Study in 2011, 2013 and 2015. Random-effects logistic regression models were applied to estimate the association between multimorbidity and primary healthcare as well as admission to the hospital. We used log-linear regression models to investigate the association between multimorbidity and health spending. Results Overall, 46.2% of total interviewees reported multimorbidity. Random-effects logistic regression analyses showed that multimorbidity was associated with a higher likelihood of medication use (Adjusted odds ratio (AOR) =19.19, 95% CI = 17.60, 20.93), health check (AOR = 1.51, 95% CI = 1.43, 1.59), outpatient care (AOR = 2.39, 95% CI = 2.23, 2.56) and admission to hospital (AOR = 2.94, 95% CI = 2.68, 3.21). Log-linear regression models showed that multimorbidity was also positively associated with spending for outpatient care (coefficient = 0.64, 95% CI = 0.59, 0.68) and hospitalization (coefficient = 0.65, 95% CI = 0.60, 0.71). Conclusions Multimorbidity is associated with higher levels of primary care, hospitalization and greater financial burden to individuals in China. Health systems need to shift from single-disease models to new financing and service delivery models to more effectively manage multimorbidity.

scholarly journals Analysis of SF-6D Health State Utility Scores: Is Beta Regression Appropriate?

Healthcare ◽  
2020 ◽  
Vol 8 (4) ◽  
pp. 525
Author(s):  
Samer A Kharroubi
Keyword(s):  
Linear Regression ◽  
Regression Model ◽  
Random Effects ◽  
Regression Models ◽  
Mean Squared Error ◽  
Health State Utility ◽  
Beta Regression ◽  
Health State ◽  
Linear Regression Models ◽  
Utility Scores

Background: Typically, modeling of health-related quality of life data is often troublesome since its distribution is positively or negatively skewed, spikes at zero or one, bounded and heteroscedasticity. Objectives: In the present paper, we aim to investigate whether Bayesian beta regression is appropriate for analyzing the SF-6D health state utility scores and respondent characteristics. Methods: A sample of 126 Lebanese members from the American University of Beirut valued 49 health states defined by the SF-6D using the standard gamble technique. Three different models were fitted for SF-6D via Bayesian Markov chain Monte Carlo (MCMC) simulation methods. These comprised a beta regression, random effects and random effects with covariates. Results from applying the three Bayesian beta regression models were reported and compared based on their predictive ability to previously used linear regression models, using mean prediction error (MPE), root mean squared error (RMSE) and deviance information criterion (DIC). Results: For the three different approaches, the beta regression model was found to perform better than the normal regression model under all criteria used. The beta regression with random effects model performs best, with MPE (0.084), RMSE (0.058) and DIC (−1621). Compared to the traditionally linear regression model, the beta regression provided better predictions of observed values in the entire learning sample and in an out-of-sample validation. Conclusions: Beta regression provides a flexible approach to modeling health state values. It also accounted for the boundedness and heteroscedasticity of the SF-6D index scores. Further research is encouraged.

scholarly journals Does healthcare inequity reflect variations in peoples’ abilities to access healthcare? Results from a multi-jurisdictional interventional study in two high-income countries

2020 ◽  
Vol 19 (1) ◽  
Author(s):  
Jeannie Haggerty ◽  
Jean-Frederic Levesque ◽  
Mark Harris ◽  
Catherine Scott ◽  
Simone Dahrouge ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Primary Healthcare ◽  
Social Vulnerability ◽  
Regression Models ◽  
Social Systems ◽  
Social Characteristics ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
Wide Range ◽  
Social Vulnerabilities

Abstract Background Primary healthcare services must respond to the healthcare-seeking needs of persons with a wide range of personal and social characteristics. In this study, examined whether socially vulnerable persons exhibit lower abilities to access healthcare. First, we examined how personal and social characteristics are associated with the abilities to access healthcare described in the patient-centered accessibility framework and with the likelihood of reporting problematic access. We then examined whether higher abilities to access healthcare are protective against problematic access. Finally, we explored whether social vulnerabilities predict problematic access after accounting for abilities to access healthcare. Methods This is an exploratory analysis of pooled data collected in the Innovative Models Promoting Access-To-Care Transformation (IMPACT) study, a Canadian-Australian research program that aimed to improve access to primary healthcare for vulnerable populations. This specific analysis is based on 284 participants in four study regions who completed a baseline access survey. Hierarchical linear regression models were used to explore the effects of personal or social characteristics on the abilities to access care; logistic regression models, to determine the increased or decreased likelihood of problematic access. Results The likelihood of problematic access varies by personal and social characteristics. Those reporting at least two social vulnerabilities are more likely to experience all indicators of problematic access except hospitalizations. Perceived financial status and accumulated vulnerabilities were also associated with lower abilities to access care. Higher scores on abilities to access healthcare are protective against most indicators of problematic access except hospitalizations. Logistic regression models showed that ability to access is more predictive of problematic access than social vulnerability. Conclusions We showed that those at higher risk of social vulnerability are more likely to report problematic access and also have low scores on ability to seek, reach, pay, and engage with healthcare. Equity-oriented healthcare interventions should pay particular attention to enhancing people’s abilities to access care in addition to modifying organizational processes and structures that reinforce social systems of discrimination or exclusion.

scholarly journals Influence of year-on-year performance on final degree classification in a chiropractic master's degree program

2016 ◽  
Vol 30 (1) ◽  
pp. 14-19 ◽  
Author(s):  
Philip Dewhurst ◽  
Jacqueline Rix ◽  
David Newell
Keyword(s):  
Logistic Regression ◽  
Problem Solving ◽  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Logistic Regression Models ◽  
The Difference ◽  
Degree Classification ◽  
Grade Performance ◽  
Final Degree

Objective: We explored if any predictors of success could be identified from end-of-year grades in a chiropractic master's program and whether these grades could predict final-year grade performance and year-on-year performance. Methods: End-of-year average grades and module grades for a single cohort of students covering all academic results for years 1–4 of the 2013 graduating class were used for this analysis. Analysis consisted of within-year correlations of module grades with end-of-year average grades, linear regression models for continuous data, and logistic regression models for predicting final degree classifications. Results: In year 1, 140 students were enrolled; 85.7% of students completed the program 4 years later. End-of-year average grades for years 1–3 were correlated (Pearson r values ranging from .75 to .87), but the end-of-year grades for years 1–3 were poorly correlated with clinic internship performance. In linear regression, several modules were predictive of end-of-year average grades for each year. For year 1, logistic regression showed that the modules Physiology and Pharmacology and Investigative Imaging were predictive of year 1 performance (odds ratio [OR] = 1.15 and 0.9, respectively). In year 3, the modules Anatomy and Histopathology 3 and Problem Solving were predictors of the difference between a pass/merit or distinction final degree classification (OR = 1.06 and 1.12, respectively). Conclusion: Early academic performance is weakly correlated with final-year clinic internship performance. The modules of Anatomy and Histopathology year 3 and Problem Solving year 3 emerged more consistently than other modules as being associated with final-year classifications.

scholarly journals Analysis and Application of Log-Linear and Quantile Regression Models to Predict Bus Dwell Times

2019 ◽  
Vol 2673 (10) ◽  
pp. 118-128 ◽  
Author(s):  
Travis B. Glick ◽  
Miguel A. Figliozzi
Keyword(s):  
Linear Regression ◽  
Travel Time ◽  
Regression Models ◽  
Dwell Time ◽  
Time Variability ◽  
Linear Regression Models ◽  
Ongoing Research ◽  
Bus Stops ◽  
Log Linear ◽  
Travel Time Variability

Understanding the key factors that contribute to transit travel times and travel-time variability is an essential part of transit planning and research. Delay that occurs when buses service bus stops, dwell time, is one of the main sources of travel-time variability and has therefore been the subject of ongoing research to identify and quantify its determinants. Previous research has focused on testing new variables using linear regressions that may be added to models to improve predictions. An important assumption of linear regression models used in past research efforts is homoscedasticity or the equal distribution of the residuals across all values of the predicted dwell times. The homoscedasticity assumption is usually violated in linear regression models of dwell time and this can lead to inconsistent and inefficient estimations of the independent variable coefficients. Log-linear models can sometimes correct for the lack of homoscedasticity, that is, for heteroscedasticity in the residual distribution. Quantile regressions, which predict the conditional quantiles, rather than the conditional mean, are non-parametric and therefore more robust estimators in the presence of heteroscedasticity. This research furthers the understanding of established dwell determinants using these novel approaches to estimate dwell and provides a relatively simple approach to improve existing models at bus stops with low average dwell times.

INTERACTION TERMS IN POISSON AND LOG LINEAR REGRESSION MODELS

2017 ◽  
Vol 70 (1) ◽  
pp. E89-E96 ◽  
Author(s):  
Shengwu Shang ◽  
Erik Nesson ◽  
Maoyong Fan
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Linear Regression Models ◽  
Interaction Terms ◽  
Log Linear

scholarly journals Survey of prevalence and investigation of predictors and staining patterns of the split upper eyelid defect in Hebridean sheep

2017 ◽  
Vol 181 (7) ◽  
pp. 167-167 ◽  
Author(s):  
Emily Gascoigne ◽  
David L Williams ◽  
Kristen K Reyher
Keyword(s):  
Logistic Regression ◽  
Linear Regression ◽  
Regression Models ◽  
Production Cost ◽  
Linear Regression Models ◽  
Upper Eyelid ◽  
Mixed Effect ◽  
Ordered Logistic Regression ◽  
Level Data ◽  
Chi Squared

The split upper eyelid defect (SUED) is a congenital defect of the upper eyelid thought to be exclusive to multihorned sheep. Eleven flocks with a high proportion of multihorned Hebridean sheep were visited in 2011. Statistical analysis was performed generating Pearson's chi-squared analysis, as well as (1) logistic regression, (2) ordinal logistic regression and (3) linear regression models. Four hundred and seventy-three pure-bred Hebridean sheep and one crossbred lamb were examined. Of all the multihorned animals inspected in 2011, 9.7 per cent of adults had evidence of SUED in one or more eyelids, with 17.6 per cent of lambs presented with one or more eyelid affected. Having five or more horns was protective in the linear regression model on eye-level data (p=0.045). Forward-facing horns were consistently associated with a ‘worst’ eye score in the eye-level data, with an odds ratio (OR) as high as 9.4 when compared with a base of backward-facing horns (p=0.002). Eyes positive for SUED were significantly more likely to be rose bengal stain-positive in all four analysis, including multilevel mixed effect ordered logistic regression (p<0.001, OR 149.3). A novel lesion was identified during the course of the study, with 3.4 per cent of lambs presented with dermoid. SUED was also identified in a crossbred animal. Further work is needed to quantify the exact cost to animals with unilateral or bilateral SUED with subtle and production cost of SUED.

scholarly journals An ecological study to evaluate the association of Bacillus Calmette-Guerin (BCG) vaccination on cases of SARS-CoV2 infection and mortality from COVID-19

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243707
Author(s):  
Lucy Chimoyi ◽  
Kavindhran Velen ◽  
Gavin J. Churchyard ◽  
Robert Wallis ◽  
James J. Lewis ◽  
...  
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Morbidity And Mortality ◽  
World Health ◽  
Linear Regression Models ◽  
Vaccination Policy ◽  
Geographical Differences ◽  
Bcg Vaccination ◽  
Country Level ◽  
Log Linear

As the SARS-CoV2 pandemic has progressed, there have been marked geographical differences in the pace and extent of its spread. We evaluated the association of BCG vaccination on morbidity and mortality of SARS-CoV2, adjusted for country-specific responses to the epidemic, demographics and health. SARS-CoV2 cases and deaths as reported by 31 May 2020 in the World Health Organization situation reports were used. Countries with at least 28 days following the first 100 cases, and available information on BCG were included. We used log-linear regression models to explore associations of cases and deaths with the BCG vaccination policy in each country, adjusted for population size, gross domestic product, proportion aged over 65 years, stringency level measures, testing levels, smoking proportion, and the time difference from date of reporting the 100th case to 31 May 2020. We further looked at the association that might have been found if the analyses were done at earlier time points. The study included 97 countries with 73 having a policy of current BCG vaccination, 13 having previously had BCG vaccination, and 11 having never had BCG vaccination. In a log-linear regression model there was no effect of country-level BCG status on SARS-CoV2 cases or deaths. Univariable log-linear regression models showed a trend towards a weakening of the association over time. We found no statistical evidence for an association between BCG vaccination policy and either SARS-CoV2 morbidity or mortality. We urge countries to rather consider alternative tools with evidence supporting their effectiveness for controlling SARS-CoV2 morbidity and mortality.

FAKTOR DETERMINAN PENYALURAN KREDIT BANK PERSERO

2018 ◽  
Vol 23 (1) ◽  
pp. 60-71
Author(s):  
Wigiyanti Masodah
Keyword(s):  
Interest Rate ◽  
Linear Regression ◽  
Interest Rates ◽  
Regression Models ◽  
Linear Regression Models ◽  
Negative Impacts ◽  
Main Activity ◽  
The Impact ◽  
The Given ◽  
Multiple Linear Regression Models

Offering credit is the main activity of a Bank. There are some considerations when a bank offers credit, that includes Interest Rates, Inflation, and NPL. This study aims to find out the impact of Variable Interest Rates, Inflation variables and NPL variables on credit disbursed. The object in this study is state-owned banks. The method of analysis in this study uses multiple linear regression models. The results of the study have shown that Interest Rates and NPL gave some negative impacts on the given credit. Meanwhile, Inflation variable does not have a significant effect on credit given. Keywords: Interest Rate, Inflation, NPL, offered Credit.

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