Quality of life predictors for people living with HIV/AIDS in an impoverished region of Brazil

Ferreira, Aline Dayrell; Teixeira, Antônio Lúcio; Silveira, Marise Fagundes; Carneiro, Mariângela

doi:10.1590/0037-8682-0442-2017

Cited by 17 publications

(16 citation statements)

References 17 publications

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“…Higher education can also lead to better understanding of the disease and thus the higher QOL (34,45,46). Yet, two studies in Brazil reported no significant correlation between the education level and the dimensions of the QOL (44,47).…”

Section: Resultsmentioning

confidence: 99%

Quality of Life of People with HIV/AIDS in Iran: A Systematic Review and Meta-Analysis

Maleki¹,

Derakhshani²,

Azami-Aghdash³

et al. 2020

ijph

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Background: Assessing the quality of life in HIV/AIDS patients is of great importance not only for evaluating the effect of the disease, but also to measure the impact of the interventions in order to improve their quality of life in clinical researches. Therefore, this study aimed to systematically review the quality of life of HIV/AIDS patients in Iran. Methods: In this systematic review and meta-analysis, the literature search using the related chain of keywords was conducted from 1 Jan 1987 to 30 Apr 2019 in PubMed, Scopus, Web of Science, Embase, Iranian Scientific Information Database (SID), and Magiran. Moreover, hand search of the key journals and the gray literature was performed. The meta-analysis was performed by CMA2 software. Results: Out of the 1576 retrieved records, eight studies met the inclusion criteria. The average age of the patients was 37.15 ± 9.46 years. The average score of quality of life before and after sensitivity analysis was (39.13 [28.36-49.901 95% CI P>0.000] vs. 49.05 [46.31-51.79 95% CI P>0.000]). Moreover, the average score of quality of life was respectively 38.86±3.83 vs. 40±6.37 among married compared with single patients, 56.33±4.67 vs. 43.64±1.94 for employment vs. unemployment status. While quality of life was measured in terms of education level, the score was 29.59±9.34 vs. 41.65±4.45 in the individuals with primary school versus academic education. Conclusion: The QOL score of the HIV/AIDS patients in Iran was significantly low. Therefore, the study highlights the importance of strengthening efforts to undertake necessary investigations in order to provide adequate health insurance, extensive and affordable welfare services, and more appropriate social and mental supports in order to improve the quality of life of the individuals with HIV/AIDS in Iran.

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Section: Resultsmentioning

confidence: 99%

Quality of Life of People with HIV/AIDS in Iran: A Systematic Review and Meta-Analysis

Maleki¹,

Derakhshani²,

Azami-Aghdash³

et al. 2020

ijph

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“…The patients with a low QoL had the characteristics of being under 44 years of age, women, not working (so they had a low income), and having a low clinical status. The low clinic status included problems with their lungs, fever, pain, depression, and CD4 counts <500 µ/l (Ferreira, Teixeira, Silveira, & Carneiro, 2018). Other studies have also shown the same thing, namely that the majority of patients with HIV and AIDS have a good QoL.…”

Section: Quality Of Lifementioning

confidence: 84%

The Relationship of Basic Clinical Status With the Quality of Life of Hiv and Aids Patients

Masyfufah

Triyono

2020

JBE

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Background: The success of individual antiretroviral drug (ARV) treatment in patients with human immunodeficiency virus (HIV) and acquired immune deficiency syndrome (AIDS) was determined by conducting a routine evaluation of the patients’ Cluster of Differentiation 4 (CD4) count. The indicators used to measure the success of the HIV and AIDS treatment were mortality, mobility, and quality of life (QoL). Purpose: The purpose of this research was to analyze the relationship between clinical status (smoking status, duration of ARV therapy, the CD4 count, and body mass index [BMI]) and the QoL of patients with HIV and AIDS who were stable during treatment. Methods: This type of research was quantitative analytical research with a cross-sectional design. This research was conducted at Dr. Soetomo Hospital, Surabaya, from September to November 2017. The study population was patients with HIV and AIDS in Dr. Soetomo Hospital, Surabaya. The research sample was taken by purposive sampling with the inclusion criteria being patients with HIV and AIDS who had been treated for ≥6 months with adherence ≥95% and who came directly to the hospital. Results: The majority of respondents were female (53.36%), junior/senior high school graduates (66.67%), married (62.22%), non-smoking (75.56%), had undergone ARV therapy for ±10 years (77.78%), and had a QoL in the adequate category (62.22%). The basic clinical status with a significant relationship with the respondents’ QoL were the CD4 count (p = 0.00) and BMI (p = 0.00). Conclusion: There was a relationship of the CD4 count and BMI with the QoL of the patients with HIV and AIDS.

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“…Malnutrition is the condition that occurs when the body does not get enough nutrients [38]. This is determined based on the body mass index (BMI), which can be measured as weight in kilograms divided by the square of height in meters (kg/m 2 ).…”

Section: Outcome Measuresmentioning

confidence: 99%

Health-related quality of life of people living with HIV/AIDS: the role of social inequalities and disease-related factors

Nigusso

Mavhandu-Mudzusi

2020

Preprint

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Background: Health-related quality of life (HRQoL) serves as a direct measure of individuals’ health, life expectancy and the impact that the utilization of health care has on quality of life. The purpose of this study is to assess the HRQoL of people living with HIV/AIDS (PLWHA), and to ascertain its association with the social inequalities and clinical determinants among people living with HIV in Benishangul Gumuz Regional State, Ethiopia.Methods: A cross-sectional study was conducted between December 2016 and February 2017; 390 people at two referral hospitals and three health centers participated in the study. The Patient-Reported Outcomes Measurement Information System Global Health Scale (PROMIS Global 10) was used to measure key HRQoL domains. Physical Health Summary (PHS) and Mental Health Summary (MHS) scores were employed. PHS and MHS scores below 50 (the standardized mean score) were determined to be poor and above 50 to be good. Bivariate and multivariate logistic regression analyses were used to identify factors associated with PHS and MHS scores. Results: This study included 259 (66.4%) females and 131 (33.6%) males. The PHS scores ranged from 16.2 to 67.7 with a mean of 48.8 (SD = 8.9). Almost 44.6% of the study population has a PHS score of below 50; the MHS scores ranged from 28.4 to 67.6 with a mean of 50.8 (SD = 8.1). About 41.8% of the study population has an MHS score of below 50. Unemployment, household food insecurity and comorbidities with HIV were associated with both poor PHS and poor MHS scores. Age below 25 years and being a member of Christian fellowship were inversely associated with poor PHS. The least wealth index score and CD4 count below 350 cells/mL were also associated with poor MHS. Conclusion: Overall, socioeconomic inequalities and HIV-related clinical factors play an important role in improving the HRQoL of PLWHA. Many of these determinants are alterable risk factors. Appropriate strategies can improve the holistic management of chronic HIV care and maximize PLWHAs’ HRQoL. Such strategies require the adoption of comprehensive interventions, including policies and programmes that would improve the health, wellbeing and livelihood of PLWHA.

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Quality of life predictors for people living with HIV/AIDS in an impoverished region of Brazil

Cited by 17 publications

References 17 publications

Quality of Life of People with HIV/AIDS in Iran: A Systematic Review and Meta-Analysis

Quality of Life of People with HIV/AIDS in Iran: A Systematic Review and Meta-Analysis

The Relationship of Basic Clinical Status With the Quality of Life of Hiv and Aids Patients

Health-related quality of life of people living with HIV/AIDS: the role of social inequalities and disease-related factors

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