scholarly journals Quantile Regression Analysis of Modifiable and Non-Modifiable Predictors of Stroke among Adults in South Africa

2021 ◽  
Vol 14 (1) ◽  
pp. 409-416
Author(s):  
Delson Chikobvu ◽  
Lyness Matizirofa
Keyword(s):  
South Africa ◽  
Regression Analysis ◽  
Quantile Regression ◽  
Age Groups ◽  
Female Gender ◽  
Public Hospitals ◽  
Cross Sectional ◽  
Black Race ◽  
Modifiable Factors ◽  
Quantile Regression Analysis

Background: Stroke is the second largest cause of mortality and long-term disability in South Africa (SA). Stroke is a multifactorial disease regulated by modifiable and non-modifiable predictors. Little is known about the stroke predictors in SA, particularly modifiable and non-modifiable. Identification of stroke predictors using appropriate statistical methods can help formulate appropriate health programs and policies aimed at reducing the stroke burden. This study aims to address important gaps in stroke literature i.e., identifying and quantifying stroke predictors through quantile regression analysis. Methods: A cross-sectional hospital-based study was used to identify and quantify stroke predictors in SA using 35730 individual patient data retrieved from selected private and public hospitals between January 2014 and December 2018. Ordinary logistic regression models often miss critical aspects of the relationship that may exist between stroke and its predictors. Quantile regression analysis was used to model the effects of each predictor on stroke distribution. Results: Of the 35730 cases of stroke, 22183 were diabetic. The dominant stroke predictors were diabetes, hypertension, heart problems, the female gender, higher age groups and black race. The age group 55-75 years, female gender and black race, had a bigger effect on stroke distribution at the lower upper quantiles. Diabetes, hypertension and cholesterol showed a significant impact on stroke distribution (p < 0.0001). Conclusion: Most strokes are attributable to modifiable factors. Study findings will be used to raise awareness of modifiable predictors to prevent strokes. Regular screening and treatment are recommended for high-risk individuals with identified predictors in SA.

Defence Spending and Unemployment in the USA: Disaggregated Analysis by Gender and Age Groups

2020 ◽  
Vol 26 (2) ◽  
Author(s):  
Christos Kollias ◽  
Suzanna-Maria Paleologou ◽  
Panayiotis Tzeremes
Keyword(s):  
Regression Analysis ◽  
Quantile Regression ◽  
Age Groups ◽  
Age Group ◽  
Defence Spending ◽  
Gender And Age ◽  
Negative Effect ◽  
The Usa ◽  
Quantile Regression Analysis ◽  
Robust Evidence

AbstractThe paper examines the effects of military spending using disaggregated unemployment statistics by gender and age group for the period 1948–2017 in the case of the USA. Findings from quantile regression analysis do not seem to point to any robust evidence supporting the thesis that defence spending quashes unemployment levels. This finding appears to be the case across all groups of unemployed persons. In fact, the results suggest a negative effect on unemployment.

scholarly journals Identification of factors affecting the survival lifetime of HIV+ terminal patients in Albert Luthuli municipality of South Africa

2021 ◽  
Author(s):  
Pepukai Bengura ◽  
Prince Ndlovu ◽  
Annah Mulalo Managa
Keyword(s):  
South Africa ◽  
Regression Analysis ◽  
Viral Load ◽  
Quantile Regression ◽  
Baseline Viral Load ◽  
Art Adherence ◽  
Factors Affecting ◽  
Regression Modelling ◽  
Quantile Regression Analysis

Abstract Background: South Africa has the biggest HIV epidemic in the world, with Mpumalanga province in which Albert Luthuli municipality is located having the second highest HIV prevalence rate after KwaZulu-Natal province. The objective of the study was to identify the factors that affect the survival lifetime of HIV+ terminal patients in rural district hospitals of Albert Luthuli municipality.Methods: This is a typical retrospective cohort longitudinal design study whereby cohort of HIV+ terminal patients was retrospectively followed from 2010 to 2017 until a patient died, transferred to another hospital, lost to follow-up or was still alive at the end of the observation period. The follow-up time for each patient started at the time the patient got initiated to the ART programme at the hospital’s wellness centre. Nonparametric survival analysis and semiparametric survival analysis methods were used to analyse the data.Results: Through Cox proportional hazards regression modelling, it was found that ART adherence (poor, fair, good), Age, Follow-up mass, Baseline sodium, Baseline viral load, Follow CD4 count by Treatment (Regimen 1) interaction and Follow-up lymphocyte by TB history (yes, no) interaction had significant effects on survival lifetime of HIV+ terminal patients (p-values < 0.1). Furthermore, through quantile regression modelling, it was found that short, medium and long survival times of HIV+ patients, respectively represented by the 0.1, 0.5 and 0.9 quantiles, were not necessarily significantly affected by the same factors.Discussion: The Cox PH modelling and the quantile regression analysis complemented each other in answering the research question. However, although the Cox PH modelling was the main approach in this study, the quantile regression analysis results are more informative than the Cox PH modelling results.Conclusion: The study identified and modelled the factors affecting the survival of HIV+ terminal patients in Albert Luthuli Municipality by using Logistic regression, Cox PH regression, and Quantile regression modelling. . Cox regression modelled the factors affecting the survival lifetime of HIV+ terminal patients as: ART adherence, Age, Follow-up mass, Baseline sodium, Baseline viral load and interactions of Follow-up lymphocyte by TB history and Follow-up CD4 by Treatment (Regimen 1).

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