Socio-Demographic and Clinical Predictor Variables on CD4 Cell Count Change among HIV Positive adults; a Structural Equations Modelling

ABSTACT Background: The prevalence of HIV/AIDS among adult individuals has been increasing in Sub-Sahara African countries over the last decade. In Ethiopia , the prevalence of HIV among adults was 1%. Hence, 23, 000 people were newly infected with HIV and 11,000 people were died because of AIDS related illness in 2018. The purpose of this study was to identify the most significant socio-demographic, economic, individual and clinical determinants of CD4 cell count change in HIV positive adults who initiated HAART at Felege Hiwot Teaching and Specialized Hospital, North-West Ethiopia. Methods: A secondary and retrospective study design was conducted on 792 HIV positive adults. A structural equation modeling was employed to identify the socio-demographic and clinical covariates that have a statistically significant effect on the status of CD4 cell count change. Results: L iterate patients, patients living with partner, patients living in urban area, patients disclosed the disease to family members, high income , ownership of cell, age and sex (male) were statistically significant variables. Conclusion: There was direct relation between socio-demographic variables with retention of HIV positive individuals in HAART program. There was also a direct and significant effect of clinical variables on adherence competence and adherence on CD4 cell change. Retention of patients in the HAART program had direct and significant effect on CD4 cell count change. This finding will be important for policy makers, health officials and for patients to easier access to healthcare service.


BACKGROUND
Globally, by the end of 2018, 37.9 million people were living with human immunodeficiency virus (HIV) and Sub-Saharan Africa carries the highest burden with an estimation of 71% of the global total [1].
In Ethiopia, there were 690 000 people living with HIV and HIV incidence per 1000 uninfected population over one year among all people of all ages was 0.24. The prevalence of HIV among adults was 1%. Hence, 23, 000 people were newly infected with HIV and 11,000 people were died because of AIDS related illness in 2018 [2].
Among all people living with HIV, 81% of them were on treatment and 73% of them were virally suppressed, among these, 79% knew their status, 65% were on treatment. Of all adults aged 15 years and above living with HIV, 66% were on treatment and 92% of pregnant women living with HIV accessed antiretroviral medicine to prevent transmission of the virus to their baby [2]. Women are highly affected by HIV as compared to men. Hence, among 65,0000 adults living with HIV, 410 000(63.08%) were women [3]. New HIV infections among young women aged 15-24 years were more than double those among young men (5800 new infections among young women, compared to 2000 among young men). On the other hand, HIV treatment was slightly lower among women than men, with 55% of adult women living with HIV on treatment, compared to 66% of adult men [4].
One of the measures of health status of HIV positive patients who initiated their medication is CD4 cell count change. Identifying factors affecting the level of CD4 cell count change other than ART would help health professionals and patients to facilitate proper management and monitoring of the health care intervention on individuals with highest risk (lowest CD4 cell count). Moreover, it helps to check whether HIV patients who initiated HAART based on the criteria formerly set (<=200 cells/mm3) are recovering to the normal range of CD4 count for the general healthy adult population (>=500 cells/mm3) [3].
Previous studies revealed that education is a large extent correlated with income, and much of the complexity that is evident in the relationship between income and HIV prevalence is thus also evident in the relationship between education and HIV prevalence. Relatively uneducated patients are less likely to know what AIDS is and how HIV is transmitted from one to another person [5]. Therefore, socio-demographic disadvantageous HIV positive adults are associated with lower CD4 cell count change [6]. The relationship between marital status and HIV infection is complex. Hence, the risk of HIV prevalence remained significantly high among unmarried compared with married people when only sexual behaviour factors are controlled for the given model [7].
The economic factors are related to level of income and ownership of cell phone. Income can be defined in terms of individual income or household income. There are a number of arguments that the relationship between income and CD4 cell count change which indicates that risk factors of HIV infection is higher among the poor, but there are also several arguments to explain why patterns of infection may be the other way around. Hence, there is controversies on findings from previous researches [8].
Nevertheless, it is likely that communities in which the average income is high are those living in the urban areas [9]. Previous researches indicate that there are economic benefits associated with maintaining high CD4 cell counts for HIV positive people [10]. In the previous research, it is indicated that patients aged 40 years and above presented lower rates of CD4 cell count change over the period of the cohort [11,12].
Different researches conducted recently declared that good adherence is strongly associated with obtaining optimal CD4 cell count change compared to non-adherence [13]. The adherence level of patients are highly associated with level of income such that patients with high level of income had good competence level of food and medication adherence [14]. Thus, it is said that a person is competent when he/she uses those resources to do what is expected, as would be the case in consuming medication as prescribed [15].
Retention in HIV medication care is a crucial activity for achieving long term survival with HIV infection [16]. HIV care guideline also recognizes the importance of retention in HIV care as a precursor to adherence and a further achievement of CD4 cell count [17]. Successful strategies to improve retention in HIV care and adherence to HAART require an understanding of retention and adherence behaviour and the complex interplay between biological, psychological, behavioural, social and health system drivers [18,19].
Previous researchers on predictors of CD4 cell count change used classical statistics (PROC GLM ANOVA) model. In such models, the effect of latent (unobsereved) variables on the variable of interest cannot be investigated. Of course, the additional contribution of latent variables can be studied in multiple regressions only if all independent variables are highly correlated with the dependent variable and uncorrelated themselves [18,19]. The default model for the repeated measures of CD4 cell count is PROC GLM and this assumes that change is linear, constant across time, and occurs at one unit between each measurement period and that measurement occurs without errors.
On the other hand, the SEM analysis with PROC CALIS allowed for explicit representation of measurement error, and provided more information than PROC GLM [20]. In path analysis  The purpose of this study was therefore, to identify the most significant socio-demographic, economic, individual and clinical determinants of CD4 cell count change in HIV positive adults who initiated HAART and to investigate the association between these factors using SEM. The investigation also aimed to compare the results obtained by PROC GLM and PROC CALIS.

Study design:
The data used under current investigation consists of secondary data and a retrospective longitudinal study design was employed.

Study area and Population:
The study was conducted in Felege Hiwot Teaching and Specialized Hospital, North-West Ethiopia.
Data collection procedures: Secondary data were entered and analyzed using SAS software version 9.2. For the sample to be included in the study, CD4 cell count measurement just before the initiation of HAART was considered as a covariate so that there could be at least two visit responses after the initiation of HAART for the data to be analyzed.

Quality of data:
This was conducted by data controllers in ART section of the hospital. The controllers took intensive training by the Ministry of Health for this and other purposes.
The reliability of variables was assessed using Cronbanch's alpha and the variables included in the study were tested for consistency of understanding and the completeness of the data items on75 random samples. Necessary amendments were made on the final data collection sheet. The Cronbach's alpha value for current data was 0.832 which indicates its internal consistency was good.

Variables included in the study
Response Variable: The longitudinal response variable for current study was CD4 cell count change recorded at each visiting time. CD4 cell count change was defined as the change/ or progress at each visiting time which was obtained by considering the difference between CD4 cell count at the current visit and the CD4 cell count recorded immediately before the current visit.

RESULTS:
The study was conducted on random sample of 792 HIV positive adults who initiated their HAART at Felege Hiwot Teaching and Specialized Hospital, North-West Ethiopia. Of which 401(50.6%) were females, 468(59.1%) were in urban area and the rest were in rural residents.
The descriptive parameter estimates were conducted using maximum likelihood estimation technique and indicated in Table1. Table1 revealed that | C.R| = |Estimates/S.E| was greater than 1.96 for 0.05 alpha level of confidence and greater than 2.56 for 0.01 alpha level of confidence for all covariates and this further indicates that parameter estimates for all covariates under investigation were statistically significant [26,27].
The magnitude of mean and standard deviation for each exogenous variable in Table1 indicates that standard deviation was greater than mean for all variables and this indicates that the distribution is over-dispersed.

Figure2:
In Figure3, assume that 5 is a parameter between intercept and initial value (F1) and 6 be a parameter between intercept and rate of change (F2).
Note that: the intercept is a constant equal to 1, the parameters between initial value (F1) and measured variables are assumed to be 1 [29]. Variances of measured variables are estimated at the four consecutive repeated measures of CD4 cell count change [30].
Structural slopes are fixed at 0 and 1 for CD4change_3 and CD4change_2 respectively and estimated for the CD4change_1 and CD4change [29]. Unmeasured (latent) variables are initial value (F1) and rate of change (F2). The constant intercept(C) is regressed on the latent variables (initial value) to estimate the parameter ( 5 ) and it also regressed on another latent variable (rate of change) to estimate a parameter ( 6 ) [29].
The PROC CALIS code specifies the equations that would be solved simultaneously [31]. The parameters to be estimated are the structural slope between rates of change (F2) and 4 ℎ _1( 1 ) and structural slopes between rates of change (F2) and CD4change( K2) [25].
The mean initial level ( 5 ), the mean rates of change ( 6 ), the error and disturbance variances (var 4 , var 3 , var 2 , var 1 , var 1 , var 2 ) and covariance of the disturbance terms (C 1 2 ) were estimated using PROC CALIS [25]. In Figur4, assume that a parameter between the covariate male and F1 to be 3 and between covariate male and F2 to be 4 , a parameter between a constant(C ) and F1 to be 5 , a parameter between intercept and F2 to be 6 and a parameter between intercept and covariate male to be 7 .
Similar to Figur3, it is given that the parameters originated in initial value (F1) to be fixed (1) and parameters originated in rate of change (F2) and terminated to CD4change_3 and CD4change_2 to be 0 and 1 respectively. Here, the covariate/dummy variable (male) is regressed on both initial values (F1) and growth rate (F2) [32]. The variable sex has been recorded into a variable "male" in Figure4.
The baseline model (LGM) showed acceptable fit on CFI (0.992). Unacceptable fit was found with chi-square (40.235, df = 3, p < 0.0001) and RMSEA (0.188). The covariate model including male (CLGM) found acceptable fit for CFI (0.998) and unacceptable fit for chi-square (40.42, df = 5, p < 0.0001) and RMSEA (0.14). Values for fit indices changed a small amount with the addition of the covariate (male). The differences between males and females in the parameter estimates were also investigated.
Structural slopes in the baseline models (LGM) were 0, 1, 2.08, and 2.89. Growth steps can be determined by finding differences between structural slopes [33]. The growth steps for CLGM were equal to 0, 1, 0.61, and 0.57 (0 -0, 1-0, 1.61 -1, and 2.18 -1.61) respectively. Growth steps and structural slopes provide more information about the shape of the developmental curve [33].  [36]. Retention in medication care had also direct and significant effect on HAART adherence competence which means patients which closely follow their prescribed medication given by the health staff had good adherence competence. This result agreed with previous researches [36,37]. Finally, retention in medication care and adherence competence had direct and significant effect on the variable of interest (CD4 cell count change) which has similar argument with previous researches [35,36].
The economic factors such as patients with cell phone and those who had high income associate with high retention in the medication care. Hence, patients with high income may use different alternatives to get pills and he/she also uses proper food adherence schedules for the treatment to be effective and this encourages the patient to attend the visits of health institution [38]. Cell phone of patients can play significant role in taking pills on time and to remind the date that the patient should visit the hospital and this has indirect effect on the status of CD4 cell count change. Cell phone helped patients to be HAART adherent because of its alarm (memory aid) for reminding the time pills are taken [39]. This finding is consistent with findings from another study [40] and suggests the need for making cell phones available to the needy HAART attendants. This finding is consistent with previously conducted research [9] .
Clinical factors such as patients' CD4 cells count significantly affected their retention of medication care. Patients with high baseline CD4 cell count encouraged the patient to be HAART adherent as compared to patients with less number of CD4 cell count [40][41][42][43]. This result indicates that clinical factors (baseline CD4 cell count and WHO stages) positively associated with retention of medication care.
The PROC CALIS command in current investigation helped to compute the adjusted value of latent variables (initial value and growth rate). The result of CLGM revealed that females had better initial value and growth rate as compared to males which is agreed with previous researches [40]. The potential reason for this may be the reach experience of females in taking pills for birth control leads good adherence and this further leads for better CD4 cell count progress.

CONCLUSION
The analysis in the current investigation identified a certain group of patients, such as males and The result in current investigation corroborates key factors like realistic assessment of patients' knowledge/level of education and understanding of the regimen, residence area of patients, age and sex of patients. The research endeavoured to identify certain groups that require special attention; and this helps to intervene in HAART program to be effective and elongate patients` lives with the virus Consequently, due attention should be given to address the specific needs of each group of patients. Non-adherent patients in this long-term treatment program were at risk and should receive interventional treatment. Health related education should be given to patients to be good adherent and this leads for high progress of CD4 cell count change [44,45].
Moreover, interventions need to be designed to promote early HIV testing and early enrollment of HIV infected individuals into ART services. Health related issues about socio-demographic, economic, individual characteristics and clinical factors, public awareness through advocacy and social mobilization should be included in the ART service. It is strongly recommended that underline the need for ART in HIV infected patients for immune reconstitution.

DECLARATION SECTION
Ethical approval and Consent for participants: Since the data was secondary and there was no chance of getting participants, consent for participants was not obtained from respondents.
However, to get the secondary data from the hospital in the study area, Ethical clearance certificate had been obtained from two universities namely Bahir Dar University, Ethiopia with Ref ≠ data with the current one and on the same study area. This will be the six th article for our PhD