Drug Resistance and Quality of Life and their Associated Factors for HIV Patients under Treatment at the Second Line Regimens in Public Hospitals in Amhara Region, North-West Ethiopia

Awoke Seyoum Tegegne^*
*Correspondence: Awoke Seyoum Tegegne, Department of Statistics, Bahir Dar University, Bahir Dar, Ethiopia, Email:

Keywords

Quality of life • Viral load • CD4 cell count • First-line regimes • HIV/AIDS • Opportunistic infections

Abbreviations

PLWHIV: People Living with HIV; VL: Viral Load; CD4: Classification Determinant Four; HIV: Human Immune Deficiency Virus; CI: Confidence Interval; VAS: Visual Analogue Scale, QoL: Quality of Life

Introduction

Quality of life is among the major factors involving the status of health and well-being of HIV patients [1] and improved quality of life is among the prominent goals of taking treatment [2]. Human Immunodeficiency Virus (HIV) affects immune cells, abolishing or weakening their function and Acquired Immune Deficiency Syndrome (AIDS) is the most advanced stage of HIV infection. HIV-infected patients have higher probability of being exposed to Opportunistic Infections (OIs) or developing HIV-related cancers compared to the others [3]. For the patients under treatment in first-line regimens to have a long life with the HIV virus, it is recommended to suppress viral replication with good adherence and this further leads to improvements of the quality of life of patients under treatment [4]

Previously conducted research declares that about 36.9 million people living with the HIV/AIDS virus are under treatment [5]. However, many of the HIV patients under treatment at first-line regimens do not have good health-related conditions and transferred to second-line regimens and this is a global public health concern that leads to more advanced treatment procedures [6].

In sub-Saharan Africa (SSA), about three-fifths (59%) of women and girls under treatment continue to be transferred to more complicated treatment procedures in 2019 [7]. Though young women in SSA constituted only 10% of the total population, they accounted for 24% of new HIV infections and are not in a good status of quality of life in 2019 [8].

Ethiopia is one of the most affected SSA countries with an estimated 729,089 People Living with HIV (PLWHIV) in 2018 [9]. In Ethiopia, 0.9% of the people aged 15–49 years are HIV positive, with a proportion of 1.2 and 0.6% among women and men, respectively.

Quality of life of the HIV patients under treatment is a multifactorial and multidimensional concept of physical, psychological, and social functioning and well-being [10,11]. At the same time, the impact of the factors directly or indirectly related to HIV infection needs to be considered due to the potential confounding effects of these factors on health-related quality of life [12]. Even though several studies are conducted previously on the quality of life of people under treatment, such studies vary in terms of their effects on different dimensions of the quality of life and the associated factors [13]. Some of the studies measured the quality of life of HIV patients based on either viral load or CD4 cell count or weight of patients [14]. A cross-sectional study designs were conducted previously to assess the quality of life of people living with HIV in this regard [15,16]. Assessing the quality of life of people living with HIV under treatment, especially at first-line regimens, is important for monitoring the impact of drugs and for the proper intervention of the program [17]. As far as the author’s knowledge is concerned, there is a scarcity of research conducted applying a generic Euro-Quality (EQ)-5D Three-Level (EQ-5D-3L) scale which is a WHO quality of life instrument. Therefore, the main objective of the current investigation was to assess the quality of life and associated factors for the people living with HIV at first-line regimens in government hospitals of Amhara region, Ethiopia. The current study considered two results namely, the generic EQ-5D-3L scale and treatment outcomes like Viral Load (VL), CD4 cell counts, and WHO disease stages for the assessment of the quality of life of people living with HIV under treatment at first-line regimens.

Materials and Methods

Study area and period

The study was conducted on 700 HIV-infected people under treatment at first-line regimens in 17 public hospitals, in the Amhara region, Ethiopia. Amhara region is one of the eleven regions in Ethiopia which is located in North-west Ethiopia. The participants under investigation consist of inpatients and outpatients at the firstline regimens in the Anti-Retroviral Therapy (ART) clinic in each hospital, whose follow-ups were from March 2018 to February 2021.

Study design

An institutional-based longitudinal study design was conducted to investigate factors associated with the status of the quality of life of HIV patients under treatment at first-line regimens.

Source and study population

The source population of this study was all HIV patients under treatment at first-line regimens. The study population comprised all patients with HIV/AIDS under treatment in first-line regimens who visited the ART clinic of the 17 public hospitals and fulfilled the eligibility criteria.

Sample size and sampling techniques

The sample size was computed considering the single population proportion formula and the sample size (n) was found to be 700. The secondary data obtained in charts of each patient under treatment at first-line regimens were selected by a random sampling technique. Initially, the patient came to the ART clinic in each hospital according to their appointment for a refill of medication and to check up on their health status. A stratified random sampling or proportional allocation was conducted to determine the sample size in each hospital, based on the total number of patients under treatment. Then a systematic random sampling technique was conducted using the id number of patients, to include those patients under investigation.

Study variables

Dependent variable: The dependent variable for the current investigation was the quality of life of people living with HIV and under treatment at the first-line regimens. For the assessment of the quality of life of PLWHIV, a generic EQ-5D Three-Level (EQ-5D-3L) scale and medical outcomes such as VL, CD4 cell counts, WHO disease stages, and Body Mass Index (BMI) were considered.

Independent variables: The important independent variables were categorized as socio-demographic, clinical and economic variables and individual characteristics. The categories of these variables are indicated in Tables 1 and 2 of the result section.

Study instrument and data collection procedures

In this study, the EQ-5D-3L scale and Visual Analogue Scale (VAS) were used for quality assessment. Here, EQ-5D assesses the five health domains namely mobility, self-care, usual activities, full illness/pain, and the existence of depression, and each dimension was evaluated at three levels (healthy, moderate/some extent, extremely/series problem). About 243 different health states were considered to describe the health status of the study population. These health states produce a value set (index score) ranging from 1 (for the good health state, 11111) to−0.784 (for the poor health state, 33333), where a negative sign indicates the poor quality of life of people living with HIV. The EQ-5D-3L also includes a Visual Analog Scale (EQ-VAS), which is a vertical 20 cm calibrated line ranging from 0 to 100, where the value 0 stands for poor quality and the value 100 stands for good health quality. Previous studies indicate that the two health status measures, EQ-5D-3L and EQ-VAS were validated to triangulate the quality of people living with HIV. The internal consistency was tested using Cronbach's alpha of value 0.72 for the current study. Data collection was performed in the ART section of each hospital. The data collection consisted of two parts: the first part covered the socio-demographic and clinical characteristics of the participants, while the second part consisted of the EQ-5D-3L and EQ-VAS. All these were done using the charts of HIV patients in the ART section of each hospital. Data were collected by the health staff with a close follow ups of an investigator. Orientation about the variables included under this study was given to data collectors by the investigator. The sensitivity of the generic EQ-5D-3L was used to differentiate the quality of life measured interims of treatment outcomes such as VL, CD4 cell outcomes, and the like. This approach has great advantages in generating health utility scores used to measure economic evaluation [18].

Model adequacy checking

The important assumption of linear models such as the linear relationship between dependent and independent variables, the independence of all observations, normality, homoscedasticity, and normality and random distribution of the error terms were investigated. The Durbin-Watson test was conducted for the investigation of the serial correlation of the residuals. Case-wise diagnostics were also conducted to test the selection criterion for outliers above standard deviation. Cooks distance was conducted for overall measures of influence on all parameters and was conducted to test the goodness-of-fit statistics.

Data analysis

The WHO quality of life of people living with HIV was used to investigate the quality of life of people living with HIV under treatment in the first-line regimens. The expected scores of items within each domain were used to calculate the domain score. Data analysis was conducted using the SAS software. Descriptive statistics (frequency, mean and SD) were used for frequency distributions of responses.

Results

Socio-demographic information

Out of a total of 700 HIV-positive people, 53.6% of them were males, 54.6% of them were living without their partners, 68.6% of them were educated, 63.1% were virally unsuppressed, and 77.3% of them were urban residents. The majority of them (81.4%) were at working status. Among participants included in this investigation, about 32% of them did not disclose their HIV status to family members. In this investigation, about 35% of the participants were at WHO clinical stages IV and about 6% of the patients were HIV/TB coinfected at the enrolment stage. About 2.4%, 3.9%, and 4% of the patients were alcohol consumers, smokers, and opportunistic with pneumonia, Salmonella infection, candidiasis, toxoplasmosis, and Tuberculosis (TB) respectively. The baseline characteristics of the participants on categorical variables are indicated in Table 1.

Similar to the categorical variables, continuous variables were also summarized in this investigation. In this regard, the median weight of the patients was 48 kg with Inter-Quartile Rage (IQR: (52, 64)), and the median age in years of all patients was 30.67 years with IQR: (22, 38).

Table 1. Baseline characteristics and categories of socio-demographic variables (n=700).

Clinical variables during HAART

The clinical outcome variables at the initiation of the first-line regimens and each visiting time of the first-line regimen were also recorded. The clinical outcome variables recorded at the end of the study time as indicated in Table 2.

Table 2. Baseline characteristics and categories of clinical variables (n=700).

As it is indicated in Table 2, about 67.3% of the patients got treatment failure (low CD4 cell count and detectable VL), 77.3% of the patients were at working status, 60.7% of the patients under treatment were non-adherent, 23% of the patients did not disclose to people living with them, 56% of them faced opportunistic with other infectious diseases and 55.5% of the patients faced drug toxicity. To identify the covariates that should be included in the multivariate data analysis, univariate data analysis was conducted as shown in Table 3. As it is indicated in the univariate data analysis in Table 3, the covariates age of patients and CD4 cell count at initiation of the second-line regimen were significant at 25% CI. Similarly, the factors such as patient category, marital status, sex of patients, opportunistic infections, the existence of mental depression, disclosure of HIV status, residence area, level of education, adherence to Highly Active Anti-Retroviral Therapy (HAART), HIV functional status, smoking status alcohol intake status, drug toxicity, and WHO clinical stages were also significant at 25% CI. Hence, all these variables should be included for multivariate data analysis in the identification of associated factors for treatment failure. Among those patients who disclosed their disease status, a considerable number of them (65%) declared social support was given to them by families and communities around them. Similarly, an incidence of mental depression among the participants was also inventoried using Beck’s depression inventory scale at each visit and the result showed that 27 (3.7%) of them were mentally depressed.

The nature of the missingness pattern in the current investigation was tested using a logistic regression model which is known to be monotone (dropouts). The pattern indicates that there were no missing observations in the first two visits and the number of dropouts increased linearly as follow-up times/visits increased. The result, in this regard, revealed that dropouts were not affected by the previous outcomes (t=0.5017, p=0.862). Hence, the missing ness pattern was Missed Completely at Random (MCAR).

Model adequacy checking result

Standard multiple regression analysis was used to assess the ability of socio-demographic and clinical characteristics to predict the quality of life of people living with HIV. To assess normality, linearity, multicollinearity, and homoscedasticity, preliminary analyses were conducted. A goodness of fit model was conducted for multivariate linear regression with F=188.12 and P<0.01, adjusted R2=0.781. The univariate data analysis related to health status indicator is indicated in Table 3.

Table 3. Univariate data analysis on health status indicators for people living with HIV (n=700).

Table 3 indicates that the overall mean of EQ-5D utility score was 0.432 with a standard deviation of 0.25. Of the 700 participants, about half of them (56.0%), reported that they are in workable status and about 29% of the patients were bedridden. The expected number of viral load was 658 copies/mm³ with a standard deviation of 54 copies/mm³ and the expected number of CD4 cell count for all of the patients was 168 cells/mm³ with a standard deviation of 35.2 cells/ mm³. Among the participants, about 35% of them can’t take care of themselves and about 40% of the participants had depression/anxiety problems.

The generic Euro quality of life and visual analog scale for each of the categories of covariates was investigated as shown in Table 4.

Table 4 indicates that the overall mean of EQ-5D-3L vs. Visual Analog Scale (EQ-VAS) score was 66.20 with SD=17.22. Generally, different indicators for quality of life were observed across the entire participants. Among the health indicators, about 68 (11.3%) of the respondents reported a completely healthy state (state: 11111) with an index value of 1 and with no loss in any domain, about 86 (14.3%) of the participants indicated a moderate problem in the first four domains and severe loss in the fifth domain (state: 22223, index value: 0.764), while 23 (3.8%) reported poor health state (state: 33333, the index value of −0.594) with extreme loss in all the five domains of EQ-5D-3L (Table 4).

Table 4. Health status classification for people living with HIV (n=700).

Table 5 indicates that there was a significance difference in quality of life between variables/among categories of variables.

Table 5. Results of EQ-5D difference scores by sample characteristics (n=700).

The univariate and multivariate linear regression results in Table 6 indicate that the EQ-5D tools significantly differentiate the quality of life of people living with HIV. Hence, there was significance difference between males and females, children and adults, living with partners and without partners, educated and illiterate, employed and unemployed, workable, ambulatory and bedridden, adherent and non-adherent, substance users and non-users. Hence, gender, age of patients, marital status, level of education, employment status, HIV functional status, medication adherence status, disease disclosure status, opportunistic inactions, and substance use status significantly affected the quality of life of people living with HIV (Table 6). The normality of the data was assessed using QQ-plot and Shapiro-wilk test (p>0.05). Model statistics, F=178.8; p<0.001; R²=0.885 and adjusted R2=0.881.

Table 6. Univariate and multivariable linear regression data analysis in identifying predictors for quality of life (n=700).

The multivariate data analysis in current study indicated that, male patients had better quality of life as compared to females. Hence, the expected difference in quality of life of male and female patients was 5.19 ± 0.62 (p-value<0.001) keeping the other conditions constant.

Age was another significant variable affecting the quality of life HIV patients at first line regimen. Comparing children with adults, the expected quality life difference for the two groups was 7.5 ± 7.5 (pvalue< 0.01) given the other conditions constant.

Marital status was significantly affected the quality of life for HIV patients under first line regimens. The expected quality life different between patients living with and without a partner was 12.5 ± 10.3, keeping the other conditions constant.

Educated patients had better quality of life as compared to uneducated/illiterate patients. Hence, the expected difference in quality of life of educated and uneducated patients was 22.5± 4.7, keeping the other conditions constant.

Employment status affected the quality of life of HIV patients under first line regimens. Employed patients or patients with enough level of income had better quality of life as compared to unemployed patients. The result in Table 6 indicates that the expected quality life difference between employed and unemployed patients was 7.5 ± 11.7, given the other covariates constant.

HIV functional status also affected the quality of life of people living with the HIV. Patients at working functional status had better quality of life as compared to ambulatory and bedridden patients. Similarly, visiting time significantly affected the quality of life of patients. As patients had frequent visits as per the given the given prescription, the quality of such patients also significantly improved.

Adherent patients had better quality of life as compared to nonadherent patients. Hence, the expected difference in quality of life of adherent and non-adherent patients was 27.5± 5.1, given the other covariates constant.

Patients without opportunistic infections had better quality of life as compared to patients with opportunistic infections. The expected difference in quality of life of patients with and without opportunistic infections was 20.5 ± 2.7, given the other covariates constant.

The status of disclosure of the HIV disease also significantly affected. The expected difference in quality of life of patients who disclosed and did not disclose the disease status was 3.49 ± 0.3, given the other covariates constant.

Patients addicted with substance had low quality of life as compared to those patients without substance use. The expected difference in quality of life of HIV patients without substance users and users was about 23 ± 1.7, given the other covariates constant. Furthermore, the present study has indicated that suppression of viral load is a good indicator for better quality of life of people living with HIV.

Discussion

Important variables significantly affected the quality of life of people living with HIV have been identified in the current study. Hence, sex of patients, age of patients, employment status, level of education, opportunistic infections, disclosure status of the disease, marital status, HIV functional status, visiting times, adherence status, and substance use significantly associated with the quality of life of people living with HIV under treatment in first line regimens.

Regard to sex of patients, male patients had better quality of life as compared to females and this result is supported by one of the previous studies [19]. The potential reason for this might be males have better source of income as compared to females. Hence, most of the time the economic activities in the households of the study area are authorized by males and expenditures are directed by males rather than females. The other potential reason may be the fact that all activities such as cooking and child cares are laid on the shoulder of females. In general, females are the most marginalized population, perhaps due to the high stigma and discrimination against them and this also leads for poor quality of life [20].

Comparing children with adults and old ages, children has better quality of life as children had large number of CD4 cell count as compared to adults. Large number of CD4 cells count is one of the indicators of better quality of life. This result is supported by one of the previous study. The previous study states that in individuals over 50 years of age with HIV, it is worthwhile to explore the presence of co morbidities, frailty, anxiety, and depression to improve the survival and health related quality of their life.

Marital status significantly affects the quality of life of HIV patients. Patients living with their partners have better quality of life as partners may help each other in taken medication according to the prescription given by the health. This means, partners can be served as a reminder to take pills, food and other related medication timely. This result is supported by one of the previous study and contradicted by another study. The study result in the contradicted research stated that patients living together make frequent sexual intercourse and this leads for extra infections and poor quality of life. The result obtained from the previous study declared that the average quality of life of people living with HIV score for singles was more than married and widowed patients, which is contradicted with this research. Another study states that being in a relationship is a good predictor of quality life as it lowers the chances of loneliness and depression. Hence, this result needs further investigation for consistency of the result.

Education plays significant role for the variation of quality of life of HIV patients at first line regimens. Educated patients have better understanding the use of strict adherence of medication and give emphasis for their life. Educated people also know what is better and what is worst related to their life as compared to uneducated ones. This result is supported by the research conducted previously.

Employment status affects significantly on the quality of life people living with HIV under treatment. Employed patients may have sustainable income and can access better availability food and medication as compared to unemployed ones. This result is supported by another study. Another study supported the result by stating it as employment could be linked to higher social well-being of people living with HIV.

HIV functional status also affects the quality of life of people living with the HIV. Patients at working functional status had better quality of life as compared to ambulatory and bedridden patients. The potential reason for this might be patients at ambulatory or bedridden have low number of CD4 cell counts and may have detectable viral load and worst WHO HIV stages (Stage IV or stage III). This result is supported by another previously conducted study.

Adherent patients who frequently visited the health institutions based on the prescription given by the health staff have better quality of life as compared to non-adherent patients. Most of the time nonadherent patients lack to take medication on time and they may drink alcohol and other substance. Such conditions may make interaction effect and leads to poor quality of life and treatment failure. Previous study declared that only adherent patients and adherence is linked to viral suppression and subsequent asymptomatic state for better quality of life for HIV patients under treatment at first-line regimens.

Patients free from any other opportunistic infection have better quality of life as compared to those who have opportunistic infections. Opportunistic infections have their own side effect for the quality of life and the drug taken for the opportunistic infections may affect the effectiveness of the ART drug. This result is supported by other study.

The status of disclosure of the HIV disease also significantly affects the quality of life of HIV patients under treatment at first-line regimens. Patients disclosed their disease status can take the drug timely with irrespective of people around them. They do not worry about the man around them as the man around them is aware of the disease. This result is supported by other studies. Patients addicted with substance had low quality of life as compared to those patients without substance use. Patients who are addicted with substance may forget the time to take drugs. Even if he/she take the drug on time the substance they take may affect the effectiveness of the drugs taken.

High level of depression significantly affects the quality of life HIV patients under treatments at first-line regimens. Patients who have severe depression and anxiety may not be happy with their life, may not take medication timely and this leads for poor quality of life. Previous studies have shown that more than 10% of anxiety and depression are key factors affecting the quality of life of patients. Therefore, psychological interventions are needed in ART sections to improve the quality of life of HIV patients under treatment at first-line regimens. A randomized controlled trial conducted previously had reported that multi-component behavioral interventions had resulted in a significant reduction in anxiety and depression within 3 months in psychologically distressed adults

Regarding the EQ-VAS score in current study, the result obtained in this study is consistent with another previously conducted study which is reported as VAS median score was 90 for people living with HIV. Regarding the sensitivity of EQ-5D, patients with WHO stages III or IV significantly lower the quality of life of people living with HIV.

The two results obtained from EQ-5D VAS and quality of life measured using domains and CD4 cell counts >200 cells/mm3 are approximately similar. Hence, patients with high expected quality life have high number of CD4 cell counts, undetectable viral loads and low stages of WHO HIV stages. Similarly, other studies have reported CD4 lymphocytes count as an important predictor of other domains of the quality of life of people living with HIV under treatment.

Furthermore, suppression of viral load is a good indicator for better quality of life of people living with HIV. Several other studies have declared that viral suppression leads to enhance physical health and mental health, and this can only be accomplished through a consistent lifelong adherence to ART. Another advantage of viral suppression is that it stops the transmission of HIV to a non-HIV partner.

Conclusion

The study indicated that medication and related adherent for people living with HIV in the study area had better quality of life as compared to non-adherent patients. The two results obtained from EQ-5D VAS and quality of life measured using domains and CD4 cell counts >200 cells/mm³ are approximately similar. Hence, patients with high expected quality life have high number of CD4 cell counts, undetectable viral loads and low stages of WHO HIV stages.

The instrument used to assess the quality of life (the EQ-5D-3L) was found to be sensitive in detecting quality of life differences. The EQ-5D-3L instrument considered HIV clinical stages, number of CD4 cell counts and viral loads.

Hence, male HIV patients, older age (adults), illiterate patients, HIV patients living without partner, patients with detectable viral load (>1000 copies/mm³), unemployed patients, patients with low CD4 cell count, rural patients, HIV patients with opportunistic infection, HIV patients not disclosed their HIV status and being substance addicted were factors lead to poor quality of life of HIV patients in the study area.

The findings of the present study provide support for future work to design effective and holistic interventions that are culturally sensitive for the study area. Further studies on the determinants of health outcomes in this vulnerable population are also warranted.

Limitations

As strength, this study is the first in the study area with large study sites (17 public hospitals) and enough number of participants. The important point that can be considered as strength of current study is that, EQ-5D VAS was used in current study. This helps to compare and contrast with the results obtained from the WHO standardized tool, EuroQol questionnaire, in its sensitivity and this further helps to differentiate the quality of life of people living with HIV based on disease stage, VL and CD4 cell counts. The current findings have implications for medical decision-making to design evidence-based interventions across age groups and cultures by considering the chronic nature of HIV infection and patient reported outcomes.

This study was not without limitation. One of the limitations was patients who failed to follow up regularly based on their dispensing records were not properly recorded. Including such events may reveal additional information for policy issue.

Declaration

The data used in the current investigation were secondary and there was no chance of obtaining respondents to obtain consent to participate form from participants, because this fact, informed consent has been waived. To secure the confidentiality and compliance with the Declaration of Helsinki within the manuscript of patient-related data, the name of patients was not given to investigators, rather id number and important variables related to the current investigation were given to researchers. The waiver was done by Bahir Dar University Ethical approval committee, Ethiopia with reference number: RCS/1412/2021. Hence, the Bahir Dar University Ethical Committee approved and waived this study.

Acknowledgments

All the health staff at each government hospital is gratefully acknowledged for the data they supplied for our health research.

Author Contributions

All authors made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; have agreed on the journal to which the article has been submitted; and agree to be accountable for all aspects of the work.

Disclosure

The authors declared that there are no conflicts of interest in this work.

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