Abstract
The study focused on comparison on impact of HIV/AIDS patient’s characteristics on their blood pressure in Nigeria: a case of NAUTH, COOUTH and Onitsha general hospital in Anambra State. The blood pressure being the response variables are systolic blood pressure & diastolic blood pressure, while the predictor variables being the HIV/AIDS patient’s characteristics are age, baseline count, initial weight, present weight and CD4 count of HIV/AIDS patients. The R software package was employed to facilitate the data analysis. The Multivariate Regression Model of the two response variables (Systolic PB and Diastolic PB) was first fitted with the coefficient of determination of 31.88% and 46.80% respectively for NAUTH data, 27.9% and 37.98% respectively for COOUTH data and 97.35% and 57.15% respectively for general hospital, Onitsha data. The test on the significance of the parameters for the multivariate regression for NAUTH data revealed that age and baseline count of HIV/AIDS patients have significant relationship with systolic BP at 5% level of significance, whereas other predictor variables (initial weight, present weight and CD4 count of HIV/AIDS patients) are not significant, while in the second model, only age has a significant relationship with diastolic BP, whereas initial weight, present weight, baseline count and CD4 count of HIV/AIDS patients do not have significant relationship with diastolic BP at 5% level of significance. The test on the significance of the parameters for the multivariate regression also revealed that only age has significant relationship with systolic and diastolic BP at 5% level of significance, whereas other predictor variables are not significant for both COOUTH and general hospital Onitsha data. It was further revealed that the data collected from the general hospital Onitsha has the highest coefficient of determination (0.9735) with the lowest AIC (1348.944), BIC (1374.462) and residual standard error (2.587) for systolic blood pressure model which makes the data used in this study the most suitable for the model employed under the stipulated year of study. Also observed that the same data collected from the general hospital Onitsha has the highest coefficient of determination (0.5715) with the lowest AIC (1825.917), BIC (1851.435) and residual standard error (6.008) for diastolic blood pressure model which equally makes the data used in this study the most suitable. It is clear from the result obtained in this study that the data set collected from general hospital, Onitsha from 2003 to 2017 is most appropriate for the multivariate multiple linear regression models.
Highlights
The Human Immune Virus and Acquired Immune Deficiency Syndrome epidemics are both global phenomena threatening the health of various peoples, culture and population in the world
Fitting Full Multivariate Regression Model for NAUTH, COOUTH and General Hospital. This means that the fitted multivariate regression models of Systolic and Diastolic blood pressure for NAUTH, COOUTH and general hospital, Onitsha Anambra State are respectively; E( ˆ(i) ) (i) or E( ˆ) and
Residual standard error: 6.008 on 277 degrees of freedom Multiple R-squared: 0.5715, Adjusted R-squared: 0.5638 F-statistic: 73.9 on 5 and 277 DF, p-value: < 2.2e-16. It can be observed from Output 1 that age and baseline count of HIV/AIDS patients have significant relationship with systolic BP at 5% level of significance, whereas other predictor variables are not significant
Summary
The Human Immune Virus and Acquired Immune Deficiency Syndrome epidemics are both global phenomena threatening the health of various peoples, culture and population in the world. The acronym AIDs means Acquired Immune Deficiency Syndrome. This suggests that the condition or illness is not inherited but acquired from possible environment factors such as virus infections. HIV/AIDS affects both the old, young, men and women in the society and affect the productivity of every nation From its inception this disease has destroyed lives, families and societies. Blood pressure measures cardiovascular function by measuring the force of blood exerted on peripheral arteries during the cardiac cycle or heartbeat. The best that adequately fit the multivariate multiple linear regression model
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
