Data on viral load suppression and associated factors among HIV Patients on antiretroviral treatment in Bulambuli District, Eastern Uganda

Published: 7 March 2019| Version 1 | DOI: 10.17632/9jwbg82wg7.1
Contributors:
Paul Wakooko,
Yahaya Gavamukulya,
Jayne Tusiime

Description

HIV viral load suppression (VLS) is the most important indicator of successful antiretroviral therapy. In 2016, Muyembe Health Centre IV (HCIV) in Eastern Uganda started monitoring HIV patients on ART using viral load tests in an effort to meet the third 90 of the UNAIDS 90-90-90 strategy which is VLS. The objective of this study was to ascertain progress in Bulambuli District towards achievement of VLS among HIV infected patients on ART and associated factors in order to guide the development of interventions to achieve this target. A retrospective cohort study design was used. This data provides results from 1101 medical records of HIV infected patients on ART who attended HIV clinic at Muyembe HCIV from June 2016 to April 2018. A data abstraction tool was used for data collection. The data can be summarized using descriptive statistics for categorical variables by computing proportions and means and standard deviation for continuous variables. The Chi Square test can be used to determine factors associated with VLS and logistic regression can be used to determine the magnitude by which the ART and clinical factors influence VLS. From the current study, of the patients (n=944, 85.7%) had viral load suppression at a CI of 95%. Adjusting for known confounders, only adherence to ART was a significant predictor of VLS. Individuals with fair adherence (80-95%) had 4.47 times the odds of VLS, CI=2.242-8.915, p-value of <0.0001 compared to individuals with poor (<80%) adherence, while those with good (>95%) adherence had 11.92 times the odds of VLS, CI=29.359-111.013, p-value of <0.0001 compared to individuals with poor adherence. Results therefore show that Bulambuli district is on track to attaining the third 90 of the UNAIDS 90, 90, 90 global targets by 2020 and adherence to ART is a significant predictor of VLS. This data can therefore be used to predict and simulate similar outcomes from other study settings while dealing with viral load suppression.

Files

Steps to reproduce

For the current study, the target population was HIV infected individuals on ART in Bulambuli District while the accessible population was HIV infected individuals on ART attending ART clinic at Muyembe HCIV in Bulambuli district. The study population was HIV infected individuals of all age groups, on ART for at least six months, with a viral load test done and attending Muyembe Health Centre IV from June 2016-April 2018. No records were excluded. This was a retrospective cohort study that reviewed patient records. A data abstraction tool, which has been attached, was used to extract the data. We calculated the sample size using the formula: n= Z2(1-α/2)P(1-P)N/(d2N + Z2(P(1-P)); where: n= Sample size; N= is the total number of patient records in the Muyembe HC IV; Z=is a critical value corresponding to 95 % level of confidence with a value of 1.96; P = proportion of patients who don’t attain viral load suppression at six months after ART initiation.; and d –Margin of error (precision error) = ±5%. Substituting into the formula, n = 261 (minimum sample size). Though minimum sample size was 261, we reviewed all 1101 records to increase on the precision of our estimates by reducing on the margin of error. The dependent variable was viral load suppression status (suppressed/not suppressed). Independent variables were categorized into three: Socio-demographic factors, ART related factors and Clinical factors. Data were analyzed using descriptive and inferential statistics using STATA computer software version 14. Categorical variables were summarized using descriptive statistics by computing proportions while continuous variables were summarized using means and standard deviation. In order to determine the proportion of individuals receiving ART at Muyembe HCIV who attained viral load suppression at 6 months post ART initiation, the proportion was calculated by dividing the number of patients who had achieved VLS divided by the total number of patients enrolled in the study. Similarly, in so as to determine the factors associated with poor viral load suppression among individuals on ART at Muyembe Health Centre IV in Bulambuli District, two approaches were used; first through bivariate analysis using chi square statistic and then the p-value less than 0.05 was taken to be statistically significant. Then all factors included in the bivariate analyses which were ART and clinical related factors were included in the final logistic regression model.