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High prevalence of neurocognitive disorders observed among adult people living with HIV/AIDS in Southern Ethiopia: A cross-sectional study

  • Megbaru Debalkie Animut ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    megbaru4@yahoo.com

    Affiliation Arbaminch University College of Medicine and Health Sciences Department of Nursing, Arbaminch, Ethiopia

  • Muluken Bekele Sorrie,

    Roles Formal analysis, Methodology, Software, Writing – review & editing

    Affiliation Arbaminch University College of Medicine and Health Sciences Department of Public Health, Arbaminch, Ethiopia

  • Yinager Workineh Birhanu,

    Roles Data curation, Formal analysis, Writing – original draft, Writing – review & editing

    Affiliation Bahir Dar University Colleges of Medicine and Health Sciences School of Nursing, Bahir Dar Ethiopia

  • Manaye Yihune Teshale

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Writing – review & editing

    Affiliation Arbaminch University College of Medicine and Health Sciences Department of Public Health, Arbaminch, Ethiopia

High prevalence of neurocognitive disorders observed among adult people living with HIV/AIDS in Southern Ethiopia: A cross-sectional study

  • Megbaru Debalkie Animut, 
  • Muluken Bekele Sorrie, 
  • Yinager Workineh Birhanu, 
  • Manaye Yihune Teshale
PLOS
x

Abstract

Background

Comprehensive care given to people living with HIV/AIDS is improving over time; however, their concurrent cognitive illness is still ignored, under screened and treated particularly in developing countries. And this problem is also striking in Ethiopia. Therefore, the objective of this study was to assess HIV-associated neurocognitive disorders and associated factors among adult people living with HIV/AIDS.

Methods

An institution based cross sectional study was conducted from April to May, 2017 at Gamo Gofa zone public Hospitals. International HIV Dementia Scale was used to screen HIV associated neurocognitive disorders. Logistic regression analysis was used to assess predictors of neurocognitive disorders.

Result

A total of 684 study participants were included in this study with a response rate of 98%. Among them, 56% were females while 44% were males. The mean (±SD) age of the participants was 38.8±8.8years. The screening prevalence of HIV-associated neurocognitive disorder was 67.1% (95% CI; 63.6, 70.5). Body mass index 16 kg/m2 (AOR 4.389 (1.603–12.016)), being married (AOR 0.377 (0.213–0.666), unemployment status (AOR 3.181 (1.752–5.777) and being in WHO clinical stage T3 category/advancing stages of the disease (AOR 3.558 (1.406–9.006) were the key predictors of HIV-associated neurocognitive disorders among people living with HIV/AIDS.

Conclusion

In this study the screening prevalence of HIV-associated neurocognitive disorder is higher than the earlier reports in Ethiopia and Africa. This indicates that early screening strategies and policies for cognitive health in people living with HIV/AIDS should be given a top priority.

Background

One of the most devastating health problems facing the human race in the 21 century is the Acquired Immunodeficiency Syndrome (AIDS) caused by the Human Immunodeficiency Virus (HIV) [1]. Sub-Saharan African (SSA) countries are highly affected by this disease, accounting 80% of the cases, despite the continent constituting only 11–12% of the world’s population [1, 2]. HIV is a neurotropic virus that affects the sub-cortical structure of the brain [3]. Cognitive impairment of patients due to HIV is called HIV-associated neurocognitive disorder (HAND) [4, 5]. It results in a defect in speed of problem solving ability, loss of memory and attention [6]. HAND is a collective term which comprises 3 levels of illness that ranges from asymptomatic neurocognitive impairment to minor neurocognitive disorder and frank dementia [1, 710]. Even though severe form of HAND like dementia is decreasing after the introduction of combined highly active antiretroviral therapy (cART), mild and asymptomatic form of neurocognitive disorder is still prevalent and a major problem globally [4, 1017]. Despite cART reduces the prevalence of HAND, accessing anti-retroviral therapy (ART) in sub Saharan countries is a big problem so far. Moreover the late presentation of patients in the follow-up clinic makes them vulnerable to HAND and other HIV related complications [1820].

The prevalence of HIV associated neurocognitive disorder (HAND) in adult people living with HIV/AIDS (PLWHA) ranges from 19% to 52% in developed [19, 20], and 14% to 64% in developing countries [9, 10].

HAND predisposes HIV infected patients for poor adherence, unsafe sex, substance abuse, alcohol addiction, lost to follow up and worsens their quality of life. All these factors can accelerate HIV symptoms, their progression towards AIDS defining opportunistic infections and death [7, 9, 21].

Studies carried out in Singapore, Nigeria, Cameroon, Botswana, Malawi and Dessie Ethiopia indicated that being female, old age and low educational status were an independent risk factor for HIV associated neurocognitive disorder [2, 6, 9, 10, 18, 2123], whereas, studies carried out in Brazil, Singapore and Northern Nigeria indicated that CD4 count less than 500 cells/mm3 was associated with HIV-associated neurocognitive disorder [2, 6, 18, 24]. Moreover, a study done in South Africa in 2013 showed that highly active anti-retroviral treatment (HAART) naïve and late clinical stage of the illness was factors affecting HIV-associated neurocognitive disorder [20, 23]. Body mass index, depression and alcohol abuse were also found to be associated with HIV-associated neurocognitive disorder in Uganda [1]. In addition to the above factors, the presence of opportunistic infections and poor medication adherence were associated with HIV-associated neurocognitive disorder [8, 25].

In Ethiopia, particularly in the study area, there is deficient information regarding the prevalence of HIV-associated neurocognitive disorder and its associated factors among peoples living with HIV/AIDS though there are many people living with HIV/AIDS[8].

Therefore, this study will help to determine the prevalence and the most important associated factors which have an impact on HIV-associated neurocognitive disorders among peoples living with HIV/AIDS.

Methods

Study design, setting and area

An institution based cross-sectional study was conducted from April to May 2017 in the Gamo Gofa zone, which is located at a distance of 505 km from Addis Ababa (the capital of Ethiopia). It is administratively organized in 15 Woreda, 2 administrative towns, 34 urban and 452 rural kebeles. The total populations are 2,040,972 according to the 2017 Zonal report. There are 3 public hospitals, 73 health centers and 471 health posts in the Gamo Gofa zone currently functional according to unpublished zonal health officials’ report 2017.

Study population

The target population was PLWHA aged between 18 and 64 years who had treatment follow up at Gamo Gofa zone public Hospital ART clinics. Those selected PLWHA who was getting ART service during the data collection period were included in the study. PLWHA who were seriously ill during the study period, known psychiatric and neurological problems (depression, psychosis and epilepsy/seizure disorders) were excluded from the study.

Sample size and sampling procedures

Sample size was determined by taking the two significant variables from previous studies which was CD4 count less than 500 cells/mm3 and having a primary educational level or less in a study done at South Wollo, Ethiopia [8, 22] with 80% power, 0.36 odds ratio, 95% CI and a none response rate of 10%. Based on this assumption, the final sample size was 697. Systematic random sampling was employed to select study participants. From 3 Hospitals, samples were allocated to each Hospital proportionally based on their number of PLWHA.

Finally, PLWHA in every 3rd PLWHA from their regular follow up in the hospital was enrolled in the study by systematic random sampling method.

Data collection tools and procedures

A structured questionnaire was used to collect data on socio- demographic characteristics of the participants and clinical factors of HIV/AIDS (WHO stages, CD4 count…etc.). The WHO staging system uses the laboratory method to categorize the immune status of HIV/AIDS patients by their total lymphocyte counts (CD4) and clinical markers believed to have prognostic significance resulting in four categories. WHO stage I, II, III & IV before the initiation of cART and WHO stage T1, T2, T3 & T4 six months after cART initiation. T stands for treatment. This staging system has proven reliable for predicting morbidity and mortality in HIV/AIDS patients especially in developing countries where sophisticated laboratory investigations are inaccessible [26]. Other variables that were important for the objective were extracted from the patient's chart.

International HIV Dementia Scale (IHDS) was used as a screening tool to identify individuals who are at risk of HAND. The presence of HAND was screened by an IHDS score of <9.5 in this study. This tool has been validated in Sub-Saharan African countries, particularly in South Africa, Cameroon, Botswana, Uganda and Ethiopia and found to have good psychometric properties in African populations with sensitivity of 88% and 80% and specificity of 50% and 55% at a cutoff 10 or less [1, 9, 10, 20]. HAND consists of the three subsets of tests, namely motor speed, psychomotor speed and memory-recall test. The procedure was as follows:

Motor Speed test.

It was measured by instructing the participant to open and close the first two fingers of his/her non-dominant hand as widely and as quickly as possible over a 5-second period. The score was given as follows (4 = 15 in 5 seconds 3 = 11–14 in 5 seconds, 2 = 7–10 in 5 seconds, 1 = 3–6 in 5 seconds, 0 = 0–2 in 5 seconds).

Psychomotor speed test.

In this procedure participant was ordered to perform the following activities by their non-dominant hand within 10 seconds as fast as possible. First clench their fist on a flat table secondly put the hand flat on the table with the palm down and finally make their flat hand perpendicular to the table on the fifth digit side. This procedure was demonstrated to the participant by the data collector and the participant was allowed to practice the procedure correctly before actual 10 second sequence was performed. The number of sequences correctly performed within 10 seconds up to a maximum number of 4 is scored (4 = 4 sequences in 10 seconds, 3 = 3 sequences in 10 seconds, 2 = 2 sequences in 10 seconds, 1 = 1 sequence in 10 seconds and 0 = unable to perform).

Memory-Recall.

In this procedure participants were asked to repeat four words after reciting by the data collector. The words are repeated by the data collector until the participant can repeat all four words correctly. The participants were asked to recall the four words after the above two procedures. For words not recalled, the participants were reminded with a ‘semantic’ clue as follows: Animal (Cow), a piece of cloth (T-shirt), Vegetable (cabbage), and color (Blue). 1 point was given for each word spontaneously recalled and 0.5 points for each correct answer after semantic clues and the maximum point were scored out of 4. Finally IHDS score was calculated by adding all three subsets out of 12 points and participants who scored <9.5 were screened as having a risk of HAND in this study.

Adherence of patients to their medication was assessed using Morsiky-8 item scale (MMAS).

The MMAS-8 is a self-report questionnaire with 8 questions (items). Items 1 through 7 have response choices “yes” or “no” whereas item 8 has a 5-point Likert response choices. Each ‘‘no” response is rated as ‘‘1” and each ‘‘yes” is rated as ‘‘0” except for item 5, in which each response ‘‘yes” is rated as ‘‘1” and each ‘‘no” is rated as ‘‘0”. For item 8, if a patient chooses a response ‘‘0”, the score is ‘‘1” and if they choose response ‘‘4”, the score is ‘‘0”. Responses ‘‘1, 2, 3” are respectively rated as ‘‘0.25, 0.75, 0.75”. Total MMAS-8 scores can range from 0 to 8 and have been categorized into three levels of adherence: high adherence (score = 8), medium adherence (score of 6 to < 8), and low adherence (score< 6) [8, 27].

Alcohol use disorder identification test.

The Alcohol Use Disorder Identification Test (AUDIT) is a 10-item instrument designed by the World Health Organization to screen excessive consumption, hazardous, and harmful use of alcohol. The instrument has been validated in many countries, including Sub-Saharan African countries. A score of 8 and above is indicative of either harmful use or dependence [2]. And a cut of point > = 8 was used in this study.

Drug abuse screening test.

Drug Abuse Screening Test (DAST) instrument is designed to screen for drug use other than alcohol [28].The DAST-10 version was used in this study and is answered in a ‘‘yes or no” pattern. A score of 3 and above is indicative of a problem with psychoactive substance abuse [2].

Data quality management

Data collectors were trained for two days by doing standardization exercise in order to minimize errors. The questionnaire was developed in English and then translated into Amharic (local language) and back to English then review was made for its consistency. Data was collected after pretest has been conducted on 35 (5%) of PLWHA from non-selected health institutions. The principal investigator made day to day on-site supervision during the whole period of data collection. The collected data were reviewed and checked for completeness, accuracy and consistency by the investigator.

Data management and analysis

Data entry and cleaning was made using Epidata 3.1 and exported to SPSS software package version 20 for analysis.

Cross-checking and data cleaning was carried out by running frequencies of each variable. For specific objective one, descriptive statistical methods such as frequencies, percentages, proportion with 95% C.I has been used. Mean and standard deviation was also used to summarize various characteristics of the participants. For specific objective two, cross tabulation and bivariable logistic regression were used to explore the relation between the outcome variable and the different independent variables using crude odds ratio with 95% C.I. Finally, to determine the independent factors associated with the outcome variable, multivariable logistic regression model were done, and presented using adjusted odds ratio (AOR) with 95% confidence interval. Variables with P-value < 0.2 of the bivariable analysis were taken in to the multivariable logistic regression model. Model fitting was checked using log likelihood and Hosmer-Lemeshow test. Finally, variables with P < 0.05 in the multivariable analysis were considered as significant.

Results

Socio-demographic characteristics

Six hundred eighty four (684) people living with HIV/AIDS were involved in this study, making the response rate of 98%. The mean age of the respondents was 38.8±8.8 years. Among the study participants majority 383 (56.0%) were females and 284 (41.5%) were widowed (Table 1).

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Table 1. Distribution of PLWHA by their socio-demographic characteristics at Arbaminch general hospital, Chencha & Saula district hospitals ART clinic, 2018.

https://doi.org/10.1371/journal.pone.0204636.t001

Clinical status

Most of the participants 589 (86.1%) were in WHO clinical stage T1 category and their mean CD4 count was 610 ± 278 cells/mm3. And 95.6% of the participant had no any history of central nervous system related opportunistic infections. Adherence of participants with their ART medication was assessed using Morsiky-8 item scale (MMAS). And MMAS ranges from 0–8 points. The majority of the participants 408 (59.6%) were low adherent to their ART medication i.e. they scored <6 points by this scale (Table 2).

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Table 2. Distribution of PLWHA by their clinical status at Arbaminch general hospital, Chencha & Saula district hospitals ART clinic, 2018.

https://doi.org/10.1371/journal.pone.0204636.t002

Alcohol and other drug usage pattern

Alcohol use disorder identification test was used to screen excessive consumption, hazardous, and harmful use of alcohol. And in this study majority 624 (91.2%) of the participants never used alcohol excessively, hazardously or harmfully. Drug abused screening test (DAST-10) was also used to screen participants for their drug usage other than alcohol and most of the participants 628 (91.8%) scored between 0–3 which is non-drug abuse.

Prevalence of HIV associated neurocognitive disorder (HAND)

Among the study participants who have been screened for HAND, 459 (67.1%, 95% CI; (63.6, 70.5)) tested positive for HAND. Procedural implementation of international HIV dementia scale (IHDS) was as follows: The first measurement on IHDS was timed finger tapping, on this part motor speed was assessed, 65 (9.5%) were scored 4 out of 4 which is normal. On the second part psychomotor speed measurement was assessed, of whom 109 (15.9%) had performed the sequential procedure, scoring 4 out of 4. Finally, memory recall was assessed and 174 (25.4%) had recalled all the four items without any clue scoring 4 out of 4.

Factors associated with HAND

Bivariable and multivariable analysis showed that the association between HIV-associated neurocognitive disorder and marital status, body mass index, WHO clinical status and occupational status were significant. After controlling /adjusting other variables like (sex, alcohol abuse, religion, educational status, CD4 count, drug abuse, duration with ART, medication adherence, monthly income, comorbid illness, opportunistic infection history). The odds of HAND among married participant decrease by 62.3% compared to those with unmarried/single participants (AOR = 0.377 CI (0.213–0.666)).

In this study the likelihood of having the risk of HAND among peoples living with HIV/AIDS (PLWHA) was four times in those participants who had a body mass index of less than 16 kg/m2 than those participants who had a body mass index of 18.5–24.9 kg/m2 (AOR 4.389 CI (1.603–12.016).The odds of having HAND among unemployed participants were three times higher than those who were employed (AOR = 3.181 CI (1.752–5.777)). Similarly, late stage of the disease or being in WHO clinical staging T3 category was approximately four times more likely to have the risk of HAND (AOR = 3.558 CI (1.406–9.006)) when compared to those with WHO clinical staging T1 category (Table 3 below).

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Table 3. Bivariable and multivariable analysis showing the association between socio-demographic and clinical related variables with HAND among PLWHA in Arbaminch general hospital, Chencha & Saula district hospital ART clinics, 2018.

https://doi.org/10.1371/journal.pone.0204636.t003

Discussions

Determination of the prevalence and associated factors of HIV-associated neurocognitive disorders among peoples living with HIV/AIDS by institutional based cross sectional study in southern Ethiopia public hospitals was the main focus of this study. At the base of this, the prevalence of HAND among people living with HIV/AIDS was 67.1%. And IHDS demonstration was highly associated with marital status, body mass index, occupational status and WHO clinical stage category.

The finding of this study was in line with the study done in Sub Saharan African population which showed the prevalence of HAND (64.4%) [1], and in Kenya (65.6%) [29].

However, the prevalence of HAND in the current study was higher than a cross-sectional study done in Brazil (53%) [23], in Botswana (38%) [10] Indonesia (51%) [30], Nigeria 30% [12], South Africa (25%) [31], South Asia (22.7%) [18] and in Ethiopia at Debre Markos North West (24.8%) [25], Dessie (36.4%) [8] and the Mekele referral hospital Northern Ethiopia 33.3% [32]. The differences may be accounted for the neurovirulence strain differences and hidden sociodemographic differences. In addition, most of the above studies used smaller sample size than this study. Above all the difference in the care of patients with HIV in different settings might be different and will contribute to the difference in prevalence.

On the other hand, the prevalence of HAND in this study was lower than a cross-sectional study conducted among 400 PLWHA in Bamenda, Cameroon (85%), at Bamenda Regional Hospital AIDS-treatment Centre [9].

The discrepancy in the prevalence rate could be explained by the existence of different neurovirulent clades in different countries. Clade A and D are the most common clades/sub types in sub Saharan African countries. But in Ethiopia clade C is the prevalent sub type [33]. Even though Clade C is a neurotropic virus like A and D, it is the least common cause of neurocognitive disorder than A&D [34]. The differences in the predictors for the development of HAND might also have an effect for the discrepancy since in the previous study the common predictor was stated as having primary education or less and having HIV symptoms but not in the current study. And the discrepancy can also be explained by sample size differences; in previous study lesser sample size (400), much less than the current study was used. In addition, other possible reasons for the differences between the present and other studies were mainly due to differences in the method used, differences in study populations, using different cutoff points in different studies, some inappreciable cultural and educational difference, and environmental factors. Furthermore, discrepancies may also be explained by the application of the IHDS which might need well-educated participant.

This study found out that, marital status was significantly associated with the development of HAND. Married participants were less likely to develop HAND than single individuals. This finding is also supported by a cross sectional study conducted among 225 participants in Tanzania which indicated that living alone than married is an independent risk factor for HAND [35]. The possible reason is that married couples can promote a healthy life like to motivate each other to exercise, eat healthfully, maintain social ties, smoke and drink less [3639]. Furthermore, married couples can prevent modifiable risk factors for HAND [40, 41]. All these things could prevent the development of HAND. In single, widowed or divorced participants’ stress level will be high which further can affect nerve signaling in the brain and impair cognitive abilities [42]. In addition, people who are married tend to be financially better and stable than others, a factor that is closely associated with many aspects of human health [43].

In this study, body mass index of <16 kg/m2 was significantly associated with HAND which was nearly in line with USA Baltimore/Los Angeles sites of the Multicenter AIDS Cohort Study [44]. Severe adult malnutrition increases the incidence and severity of CNS opportunistic infections in PLWHA and in the long run patients will develop HAND [45]. Perhaps not surprisingly, the risk of different kinds of neurocognitive disorder/dementia can be increased by vitamin deficiencies, specifically B-12 [46], and other forms of Vitamin B which seems to be the vitamin most likely to potentiate the development of dementia if not consumed in adequate quantities [47, 48]. Malnutrition in PLWHA causes severe immune suppression and leads them to develop central nervous system opportunistic infections in cortical or subcortical structure of the brain, which facilitates neurocognitive disorder or dementia [45, 49]. This evidence is also supported by Cache County Dementia Progression Study which found out that nutritional status was associated with faster decline in cognitive dysfunction [50]. A longitudinal study done among 125 HIV/AIDS infected participants at The Miriam Hospital/Brown University, USA stated that malnutrition is a risk factor for neurocognitive disorder [51].

In the current study occupational status had significant association with HAND. Unemployment was an independent risk factor for the development of HAND. This study is supported by a cross sectional study done among 361 participants in Asia [52], a cohort study done in Thai [53] and cross sectional study among 604 participants in Ireland and Irish ART Clinic [54]. Employment makes a person to acquire new skills, participate in different organization and engage socially, all of these can give meaning to a person’s life and provide income [55]. In addition, these activities can boost a person’s cognitive health. Unfortunately, if they get unemployed, they will lose or limit at least employment driven cognitive health benefits [56, 57]. For people with HIV/AIDS money is used to buy different prophylactic drugs, vitamins, antiretroviral drugs and most importantly for food. In general, low socioeconomic status was independently associated with neurocognitive disorders in patients with HIV/AIDS [56, 58]. In the present study, there was statistically significant association between WHO clinical staging/late stage of the disease and HAND. This finding is supported by an observational cohort study done among 268 patients in the USA, demonstrate that this factor, was the predictive variables in the development of HAND [59]. An institution based cross sectional study conducted among 234 participants Ayder Comprehensive Specialized Hospital in Ethiopia, found out that late stage of the disease is an independent risk factor for neurocognitive disorder [32]. During late stage of the disease, there will be intense viremia both in the blood and Cerebro-spinal fluid (CSF). This can predispose patients for different central nervous system (CNS) related opportunistic infection. In addition the virus itself can replicate and multiply in different parts of the brain. Both mechanisms play a synergistic effect for the development of HIV associated neurocognitive disorders [60].

In our study adherence status of participants to their ART medication is low, even though we didn’t measure the serum concentration level of the drugs. But it wasn’t statically significant for HAND in this study. However, low adherence to ART medication is an independent risk factor for HAND in cross sectional studies done among 595 participants in South Wollo Ethiopia [8] and among 418 participants in Northern Nigeria [12]. This variable is also an independent risk factor for HAND in different studies conducted previously [11, 17, 61, 62].

But the association of HAND and adherence level has a reciprocal relationship in one systemic review [63]. A longitudinal study done among 225 participants in Boston, USA showed that adherence is highly affected by HAND [64]. Other similar studies also showed that HAND is a risk factor for low medication adherence [6567].

Even though drug abuse wasn’t statically significant in this study, from 56 abuser participants 27 participants develop HAND. But this variable was an independent risk factor for HAND in a cross sectional study done among 423 participants in Deberemarkos Ethiopia [25]. It was also an independent risk factor for development of HAND in different Studies [6870].

One of the limitations of this paper is that antiretroviral treatment regimen wasn’t recorded. It would have been better if we included this variable. Because, not all the drugs used to treat HIV penetrate the blood brain barrier (BBB) equally [7]. Regimens which contain drugs with better blood brain barrier crossing ability can suppress CNS HIV replication and prevents neurocognitive dysfunction [15, 71]. But in Ethiopia TDF-3TC-EFV are the most commonly used regimens.

Generally; the results of this study provide additional information on HAND particularly in the local areas where this study is done. The result of this study can also give some information on specific variables which affect HAND. Above all, the finding of this study will contribute about the burden and risk factor of HAND to the scientific world particularly in study areas.

Limitation of the study

Despite including participants from three hospitals it might not be applicable for all PLWHA in Ethiopia. This study included only patients on highly active antiretroviral therapy so being on HARRT might underestimate the prevalence of neurocognitive disorder. Since HARRT reduces the prevalence of HAND [7275].

Conclusion

There is a high screening prevalence of HIV-associated neurocognitive disorder than those earlier reports in Ethiopia and Africa. The associated factors also vary from that of earlier studies. Therefore, this study indicates the need for formulating preventive cognitive health programs and policies for patients with HIV/AIDS.

Ethics approval and consent to participate

Ethical clearance was obtained from an Institutional Review Board (IRB) of College of Medicine and Health Sciences, Arba Minch University & permission letter was obtained from Gamo Gofa Zonal health department and respective hospitals.

The nature of the study was fully explained to the study participants to obtain oral consent in the study and any information was kept confidential. The purpose of the study and its consequences were fully explained to the participants like the service they will get from the Hospital will be the same whether they participate in the study or not. In addition, they can stop and go if they don’t feel comfortable even in the middle of the interview. And this statement is approved by the ethics committees/IRBs. In addition, this research didn’t take any sample from the patient (like blood, urine, stool or other invasive procedures). And verbal consent was taken from the study participants in this study. Any personal identifiers were not included in the questionnaire. Any participant, who was screened as having HIV-Associated neurocognitive disorder, was recommended to be evaluated further for possible treatment and follow up evaluations back to the clinicians.

Supporting information

Acknowledgments

The authors would like to thank Arbaminch University. Furthermore, we extend our heartfelt gratitude to Gamo Gofa zone Health Bureau; we also want to thank all respondents, data collectors for their active participation during the data collection process. The last but not the least we acknowledge Mr Getaneh Alemu for his constructive comment and language editing.

References

  1. 1. Nakku J, Kinyanda E, Hoskins S. Prevalence and factors associated with probable HIV dementia in an African population: A cross-sectional study of an HIV/AIDS clinic population. BMC psychiatry. 2013;13(1):126.
  2. 2. Yusuf AJ, Hassan A, Mamman AI, Muktar HM, Suleiman AM, Baiyewu O. Prevalence of HIV-associated neurocognitive disorder (HAND) among patients attending a tertiary health facility in Northern Nigeria. Journal of the International Association of Providers of AIDS Care (JIAPAC). 2014:2325957414553839.
  3. 3. Mahadevan A, Shankar SK, Satishchandra P, Ranga U, Chickabasaviah YT, Santosh V, et al. Characterization of human immunodeficiency virus (HIV)-infected cells in infiltrates associated with CNS opportunistic infections in patients with HIV clade C infection. Journal of Neuropathology & Experimental Neurology. 2007;66(9):799–808.
  4. 4. Troncoso FT, Conterno LdO. Prevalence of neurocognitive disorders and depression in a Brazilian HIV population. Revista da Sociedade Brasileira de Medicina Tropical. 2015;48(4):390–8. pmid:26312927
  5. 5. EA H. HIV-AssocIAted NeurocogNItIVe dIsorders IN tHe combINAtIoN ANtIretroVIrAl tHerApy erA. 2014.
  6. 6. Pinheiro C, Souza L, Motta J, Kelbert E, Souza M, Martins C, et al. Depression and diagnosis of neurocognitive impairment in HIV-positive patients. Brazilian Journal of Medical and Biological Research. 2016;49(10).
  7. 7. Cross HM, Combrinck MI, Joska JA. HIV-associated neurocognitive disorders: antiretroviral regimen, central nervous system penetration effectiveness, and cognitive outcomes. South African Medical Journal. 2013;103(10):758–62. pmid:24079630
  8. 8. Tsegaw M, Andargie G, Alem G, Tareke M. Screening HIV-associated neurocognitive disorders (HAND) among HIV positive patients attending antiretroviral therapy in South Wollo, Ethiopia. Journal of psychiatric research. 2017;85:37–41. pmid:27821271
  9. 9. Atashili J, Gaynes BN, Pence BW, Tayong G, Kats D, O’donnell JK, et al. Prevalence, characteristics and correlates of a positive-dementia screen in patients on antiretroviral therapy in Bamenda, Cameroon: a cross-sectional study. BMC neurology. 2013;13(1):86.
  10. 10. Lawler K, Mosepele M, Ratcliffe S, Seloilwe E, Steele K, Nthobatsang R, et al. Neurocognitive impairment among HIV-positive individuals in Botswana: a pilot study. Journal of the international AIDS society. 2010;13(1):15.
  11. 11. Yusuf AJ, Hassan A, Mamman AI, Muktar HM, Suleiman AM, Baiyewu O. Prevalence of HIV-associated neurocognitive disorder (HAND) among patients attending a tertiary health facility in Northern Nigeria. Journal of the International Association of Providers of AIDS Care (JIAPAC). 2017;16(1):48–55.
  12. 12. Yakasai AM, Gudaji MI, Muhammad H, Ibrahim A, Owolabi LF, Ibrahim DA, et al. Prevalence and correlates of HIV-associated neurocognitive disorders (HAND) in Northwestern Nigeria. Neurology research international. 2015;2015.
  13. 13. Heaton RK, Franklin DR, Ellis RJ, McCutchan JA, Letendre SL, LeBlanc S, et al. HIV-associated neurocognitive disorders before and during the era of combination antiretroviral therapy: differences in rates, nature, and predictors. Journal of neurovirology. 2011;17(1):3–16. pmid:21174240
  14. 14. Basu S, Chwastiak LA, Bruce RD. Clinical management of depression and anxiety in HIV-infected adults. Aids. 2005;19(18):2057–67. pmid:16284454
  15. 15. Nightingale S, Winston A, Letendre S, Michael BD, McArthur JC, Khoo S, et al. Controversies in HIV-associated neurocognitive disorders. The Lancet Neurology. 2014;13(11):1139–51. pmid:25316020
  16. 16. Tedaldi EM, Minniti NL, Fischer T. HIV-associated neurocognitive disorders: the relationship of HIV infection with physical and social comorbidities. BioMed research international. 2015;2015.
  17. 17. Group MEW, Antinori A, Arendt G, Grant I, Letendre S, Muñoz-Moreno JA, et al. Assessment, diagnosis, and treatment of HIV-associated neurocognitive disorder: a consensus report of the mind exchange program. Clinical Infectious Diseases. 2012;56(7):1004–17. pmid:23175555
  18. 18. Chan LG, Kandiah N, Chua A. HIV-associated neurocognitive disorders (HAND) in a South Asian population-contextual application of the 2007 criteria. BMJ open. 2012;2(1):e000662. pmid:22331389
  19. 19. Kelly CM, Van Oosterhout JJ, Ngwalo C, Stewart RC, Benjamin L, Robertson KR, et al. HIV associated neurocognitive disorders (HAND) in Malawian adults and effect on adherence to combination anti-retroviral therapy: a cross sectional study. PloS one. 2014;9(6):e98962. pmid:24915530
  20. 20. Van Wijk C. Screening for HIV-associated neurocognitive disorders (HANDs) in South Africa: A caution against uncritical use of comparative data from other developing countries. Southern African Journal of HIV Medicine. 2013;14(1).
  21. 21. Achappa B, Priyadarshni S, Madi D, Bhaskaran U, Ramapuram JT, Rao S, et al. Neurocognitive dysfunction among HIV positive patients using International HIV dementia scale. Asian Journal of Medical Sciences. 2014;5(4):61–4.
  22. 22. Patel V, Mungwira R, Tarumbiswa T, Heikinheimo T, Van Oosterhout J. High prevalence of suspected HIV-associated dementia in adult Malawian HIV patients. International journal of STD & AIDS. 2010;21(5):356–8.
  23. 23. Souza EMd Buoniconti CS, Valim FC Moura AS. Risk factors for neurocognitive impairment in HIV-infected patients and comparison of different screening tools. Dementia & Neuropsychologia. 2016;10(1):42–6.
  24. 24. Rao VR, Ruiz AP, Prasad VR. Viral and cellular factors underlying neuropathogenesis in HIV associated neurocognitive disorders (HAND). AIDS research and therapy. 2014;11(1):13.
  25. 25. Mossie T, Tegegne M. HIV dementia among HIV positive people at Debre markos hospital, Northwest Ethiopia. Am J Psychol Neurosci. 2014;2:18–24.
  26. 26. Frehiwot N, Mizan K, Seble M, Fethia K, Tekalign M, Zelalem T. National guidelines for comprehensive HIV prevention, care and treatment. Addis Ababa: Ministry of Health. 2014.
  27. 27. Culig J, Leppée M. From Morisky to Hill-bone; self-reports scales for measuring adherence to medication. Collegium antropologicum. 2014;38(1):55–62. pmid:24851597
  28. 28. Yudko E, Lozhkina O, Fouts A. A comprehensive review of the psychometric properties of the Drug Abuse Screening Test. Journal of substance abuse treatment. 2007;32(2):189–98. pmid:17306727
  29. 29. Josua k. Prevalence of HIV aassociated dementia among HIV/AIDS adults attending comprhensive care centre-Kabsabet refereal Hospital. Journal of psychiatric research. 2017.
  30. 30. Estiasari R, Imran J, Lastri D, Prawiro-Hardjo P, Price P. Cognitive impairment among Indonesia HIV naïve patients. Neurology Asia. 2015;20(2):155–60.
  31. 31. Van Wijk C. Screening for HIV-associated neurocognitive disorders (HANDs) in South Africa: A caution against uncritical use of comparative data from other developing countries. Southern African Journal of HIV Medicine. 2013;14(1):17–9.
  32. 32. Belete T, Medfu G, Yemiyamrew E. Prevalence of HIV associated neurocognitive deficit among HIV positive people in Ethiopia: a cross sectional study at Ayder Referral Hospital. Ethiopian journal of health sciences. 2017;27(1):67–76. pmid:28458492
  33. 33. Loemba H, Brenner B, Parniak MA, Ma'ayan S, Spira B, Moisi D, et al. Genetic divergence of human immunodeficiency virus type 1 Ethiopian clade C reverse transcriptase (RT) and rapid development of resistance against nonnucleoside inhibitors of RT. Antimicrobial agents and chemotherapy. 2002;46(7):2087–94. pmid:12069959
  34. 34. McArthur JC, Steiner J, Sacktor N, Nath A. Human immunodeficiency virus‐associated neurocognitive disorders: mind the gap. Annals of neurology. 2010;67(6):699–714. pmid:20517932
  35. 35. Paddick S-M, Flatt A, Eaton P, Kellet-Wright J, Duijinmaijer A, Urasa S, et al. 16 Prevalence and risk factors for hiv-associated neurocognitive impairment (hand) amongst adults aged 50 and over attending a hiv clinic in northern tanzania. BMJ Publishing Group Ltd; 2017.
  36. 36. Berkman LF. Which influences cognitive function: living alone or being alone? The Lancet. 2000;355(9212):1291–2.
  37. 37. Yeh S-CJ, Liu Y-Y. Influence of social support on cognitive function in the elderly. BMC Health services research. 2003;3(1):9. pmid:12775218
  38. 38. Saczynski JS, Pfeifer LA, Masaki K, Korf ES, Laurin D, White L, et al. The effect of social engagement on incident dementia: the Honolulu-Asia Aging Study. American Journal of Epidemiology. 2006;163(5):433–40. pmid:16410348
  39. 39. van Gelder BM, Tijhuis M, Kalmijn S, Giampaoli S, Nissinen A, Kromhout D. Marital status and living situation during a 5-year period are associated with a subsequent 10-year cognitive decline in older men: the FINE Study. The Journals of Gerontology Series B: Psychological Sciences and Social Sciences. 2006;61(4):P213–P9.
  40. 40. Helmer C, Damon D, Letenneur L, Fabrigoule C, Barberger-Gateau P, Lafont S, et al. Marital status and risk of Alzheimer’s disease A French population-based cohort study. Neurology. 1999;53(9):1953–. pmid:10599764
  41. 41. MOUSAVI‐NASAB SMH, Kormi‐Nouri R, Sundström A, Nilsson LG. The effects of marital status on episodic and semantic memory in healthy middle‐aged and old individuals. Scandinavian journal of psychology. 2012;53(1):1–8. pmid:22092006
  42. 42. Grov C, Golub SA, Parsons JT, Brennan M, Karpiak SE. Loneliness and HIV-related stigma explain depression among older HIV-positive adults. AIDS care. 2010;22(5):630–9. pmid:20401765
  43. 43. Steinberg TC. Predictors of HIV-related neurocognitive impairment in an HIV/AIDS population: University of North Texas; 2012.
  44. 44. McArthur JC, Hoover D, Bacellar H, Miller E, Cohen B, Becker J, et al. Dementia in AIDS patients incidence and risk factors. Neurology. 1993;43(11):2245–. pmid:8232937
  45. 45. Letendre S. Central nervous system complications in HIV disease: HIV-associated neurocognitive disorder. Topics in antiviral medicine. 2011;19(4):137–42. pmid:22156215
  46. 46. Herzlich B, Schiano T. Reversal of apparent AIDS dementia complex following treatment with vitamin B12. Journal of internal medicine. 1993;233(6):495–7. pmid:8501420
  47. 47. Shor-Posner G, Feaster D, Blaney NT, Rocca H, Mantero-Atienza E, Szapocznik J, et al. Impact of vitamin B6 status on psychological distress in a longitudinal study of HIV-1 infection. The International Journal of Psychiatry in Medicine. 1994;24(3):209–22. pmid:7890479
  48. 48. Beach RS, Morgan R, Wilkie F, Mantero-Atienza E, Blaney N, Shor-Posner G, et al. Plasma vitamin B12 level as a potential cofactor in studies of human immunodeficiency virus type 1—related cognitive changes. Archives of neurology. 1992;49(5):501–6. pmid:1580812
  49. 49. Wright E, Grund B, Cysique L, Robertson K, Brew B, Collins G, et al. Factors associated with neurocognitive test performance at baseline: a substudy of the INSIGHT Strategic Timing of AntiRetroviral Treatment (START) trial. HIV medicine. 2015;16:97–108. pmid:25711328
  50. 50. Sanders C, Behrens S, Schwartz S, Wengreen H, Corcoran CD, Lyketsos CG, et al. Nutritional status is associated with faster cognitive decline and worse functional impairment in the progression of dementia: the cache county dementia progression study 1. Journal of Alzheimer's Disease. 2016;52(1):33–42. pmid:26967207
  51. 51. Okafor CN, Kelso NE, Bryant V, Burrell LE, Míguez MJ, Gongvatana A, et al. Body mass index, inflammatory biomarkers and neurocognitive impairment in HIV-infected persons. Psychology, health & medicine. 2017;22(3):289–302.
  52. 52. Rueda S, Raboud J, Mustard C, Bayoumi A, Lavis JN, Rourke SB. Employment status is associated with both physical and mental health quality of life in people living with HIV. AIDS care. 2011;23(4):435–43. pmid:21271396
  53. 53. Do TC, Kerr SJ, Avihingsanon A, Suksawek S, Klungkang S, Channgam T, et al. HIV-associated cognitive performance and psychomotor impairment in a Thai cohort on long-term cART. Journal of virus eradication. 2018;4(1):41. pmid:29568553
  54. 54. McNamara PH, Coen R, Redmond J, Doherty CP, Bergin C, editors. A High Prevalence Rate of a Positive Screen for Cognitive Impairment in Patients With Human Immunodeficiency Virus Attending an Irish Clinic. Open forum infectious diseases; 2016: Oxford University Press US. https://doi.org/10.1093/ofid/ofw242 pmid:28480240
  55. 55. Cattie JE, Doyle K, Weber E, Grant I, Woods SP, Group HNRP. Planning deficits in HIV-associated neurocognitive disorders: component processes, cognitive correlates, and implications for everyday functioning. Journal of Clinical and Experimental Neuropsychology. 2012;34(9):906–18. pmid:22731608
  56. 56. Arentoft A, Byrd D, Monzones J, Coulehan K, Fuentes A, Rosario A, et al. Socioeconomic status and neuropsychological functioning: associations in an ethnically diverse HIV+ cohort. The Clinical Neuropsychologist. 2015;29(2):232–54. pmid:25871409
  57. 57. Cross S, Önen N, Gase A, Overton ET, Ances BM. Identifying risk factors for HIV-associated neurocognitive disorders using the international HIV dementia scale. Journal of Neuroimmune Pharmacology. 2013;8(5):1114–22. pmid:24114509
  58. 58. Tadesse T, Langford D, Manji K, Mehari E. Patterns of neuroAIDS in Africa. J Neurovirol. 2005;11(Suppl 1):22–6.
  59. 59. Harezlak J, Buchthal S, Taylor M, Schifitto G, Zhong J, Daar E, et al. Persistence of hiv− associated cognitive impairment, inflammation and neuronal injury in era of highly active antiretroviral treatment. AIDS (London, England). 2011;25(5):625.
  60. 60. Halman M, Chan Carusone S, Stranks S, Schaefer-McDaniel N, Stewart A. Complex care needs of patients with late-stage HIV disease: A retrospective study. AIDS care. 2014;26(3):320–5. pmid:23869624
  61. 61. Woods SP, Dawson MS, Weber E, Gibson S, Grant I, Atkinson JH, et al. Timing is everything: antiretroviral nonadherence is associated with impairment in time-based prospective memory. Journal of the International Neuropsychological Society. 2009;15(1):42–52. pmid:19128527
  62. 62. McQuiston M. The role of cognitive functioning in medication adherence of children and adolescents with HIV infection. 2009. pmid:18647794
  63. 63. Anand P, Springer SA, Copenhaver MM, Altice FL. Neurocognitive impairment and HIV risk factors: a reciprocal relationship. AIDS and Behavior. 2010;14(6):1213–26. pmid:20232242
  64. 64. Kacanek D, Jacobson DL, Spiegelman D, Wanke C, Isaac R, Wilson IB. Incident depression symptoms are associated with poorer HAART adherence: A longitudinal analysis from the Nutrition for Healthy Living (NFHL) study. Journal of acquired immune deficiency syndromes (1999). 2010;53(2):266. pmid:20104122
  65. 65. Meade CS, Conn NA, Skalski LM, Safren SA. Neurocognitive impairment and medication adherence in HIV patients with and without cocaine dependence. Journal of behavioral medicine. 2011;34(2):128–38. pmid:20857187
  66. 66. Becker BW, Thames AD, Woo E, Castellon SA, Hinkin CH. Longitudinal change in cognitive function and medication adherence in HIV-infected adults. AIDS and Behavior. 2011;15(8):1888. pmid:21437726
  67. 67. Thaler NS, Sayegh P, Arentoft A, Thames AD, Castellon SA, Hinkin CH. Increased neurocognitive intra-individual variability is associated with declines in medication adherence in HIV-infected adults. Neuropsychology. 2015;29(6):919. pmid:25730729
  68. 68. Martin-Thormeyer EM, Paul RH. Drug abuse and hepatitis C infection as comorbid features of HIV associated neurocognitive disorder: neurocognitive and neuroimaging features. Neuropsychology review. 2009;19(2):215–31. pmid:19468837
  69. 69. Kalechstein AD, Newton TF, Green M. Methamphetamine dependence is associated with neurocognitive impairment in the initial phases of abstinence. The Journal of neuropsychiatry and clinical neurosciences. 2003;15(2):215–20. pmid:12724464
  70. 70. Simon CD L., Carnell J, Brethen P, Rawson R, Ling W, Sara . Cognitive impairment in individuals currently using methamphetamine. American Journal on Addictions. 2000;9(3):222–31. pmid:11000918
  71. 71. Brown LA, Jin J, Ferrell D, Sadic E, Obregon D, Smith AJ, et al. Efavirenz promotes β-secretase expression and increased Aβ1–40, 42 via oxidative stress and reduced microglial phagocytosis: implications for HIV associated neurocognitive disorders (HAND). PLoS One. 2014;9(4):e95500. pmid:24759994
  72. 72. Airoldi M, Bandera A, Trabattoni D, Tagliabue B, Arosio B, Soria A, et al. Neurocognitive impairment in HIV-infected naive patients with advanced disease: the role of virus and intrathecal immune activation. Clinical and Developmental Immunology. 2012;2012.
  73. 73. Daniel Elbirt M, Mahlab-Guri K, Bazalel-Rosenberg S, Attali M, Asher I. HIV-associated neurocognitive disorders (HAND). IMAJ; 2015.
  74. 74. Heaton R, Clifford D, Franklin D, Woods S, Ake C, Vaida F, et al. HIV-associated neurocognitive disorders persist in the era of potent antiretroviral therapy CHARTER Study. Neurology. 2010;75(23):2087–96. pmid:21135382
  75. 75. Ances BM, Ellis RJ, editors. Dementia and neurocognitive disorders due to HIV-1 infection. Seminars in neurology; 2007: Copyright© 2007 by Thieme Medical Publishers, Inc., 333 Seventh Avenue, New York, NY 10001, USA.