Among all the sample of 792 patients: 40.9% were rural residents; 50.6% were females; 56.3% were living with their partner; 33.6 % disclosed their disease to family members, 49.2% were owners of cell phones, 25.5% were medication adherent, only 11.5% had high income and 20.6% had no education. The average (median) weight of all patients was 58kg (IQR: (52, 64)), average years of all patients was 36 years (IQR: (28, 48)).
The estimate of regression coefficients was conducted using maximum likelihood estimation technique and indicated in hypothesised path analysis as shown in Fig. 1. Figure 1 revealed that | C.R| = |Estimates/S.E| was greater than 1.96 for 0.05 level of confidence and greater than 2.56 for 0.01 level of confidence for all covariates and this further indicates that latent variables had direct and indirect effects on the CD4 cell count change[11, 12].
Table 1
Structural Equation Model: Regression Coefficients
Variable
|
Estimates
|
Standard deviation
|
z-values
|
literate
|
0.810
|
0.900
|
25.321
|
With partner
|
0.598
|
0.773
|
21.762
|
Urban
|
0.614
|
0.783
|
22.042
|
Disclosed
|
0.293
|
0.542
|
15.245
|
High income
|
0.137
|
0.370
|
10.401
|
Owner of cell phone
|
0.556
|
0.746
|
20.980
|
age
|
-73.283
|
75.138
|
-27.445
|
Weight
|
63.713
|
65.373
|
27.426
|
Male
|
-0.490
|
0.700
|
-19.706
|
Baseline CD4 cell count
|
157.028
|
187.476
|
23.570
|
Adherent
|
0.330
|
0.574
|
16.166
|
notstage4
|
-0.784
|
0.885
|
-24.913
|
Visiting time
|
11.445
|
13.203
|
24.393
|
Socio-demographic variables
|
0.924
|
0.342
|
76.027
|
Economic factors
|
0.874
|
0.825
|
29.811
|
Individual characteristics
|
0.938
|
0.462
|
57.133
|
Clinical factors
|
0.996
|
0.883
|
31.741
|
Retention in medication care
|
0.654
|
0.742
|
24.803
|
HAART adherence competence
|
0.725
|
0.834
|
24.462
|
In the hypothetical relationships described in Fig. 1, non-recorded variables at each visiting time were categorized as socio-demographic, economic, individual characteristics and clinical factors. The magnitude of parameter estimation was conducted using AMOS version 22 and indicated in Fig. 1.
Figure1, indicates that socio-demographic factors had direct and significant effect on retention medication care (F5) with regression coefficient of 2.59 and p-value = 0.0021). Similarly, economic and individual factors had direct and significant effect on the retention medication care (F5) with regression coefficient of 2.08 for each with p-values = 0.0012 &p-value = 0.023 respectively.
The latent variable retention medication care had significant and direct effect on another latent variables HAART adherence competence (F6) with regression coefficient of 2.31, p-values = 0.0021. Similarly, clinical factors also had direct and significant effect of HAART adherence competence (F6).
Finally, the latent variables, retention in medication care and HAART adherence competence had direct and significant effect on the variable of interest (CD4 cell count change).
The hypothetical relations in Fig. 1, also indicates that the latent variables namely, socio-demographic factors (F1), economic factors (F2), individual factors (F3) and clinical factors (F4) had indirect effect on the CD4 cell count change.
Figure1 also indicates that the observed variables like level of education, residence area, marital status and disclosure of the disease to community living together had direct and significant effect on the latent variable, socio-demographic factors (F1). The observed variables, level of income and ownership of cell phone significantly affected economic factors (F2). Age and sex of patients affected significantly another latent variable (individual factors (F3)). Baseline CD4 cell count, WHO stages and adherence level significantly affected clinical factors. The observed covariate, follow up visits had significant effect on the latent covariates retention medication care (F5).
Hence, latent variables that can’t be measured directly also had direct and indirect effect on CD4 cell count change.
In the estimation of the covariance structure, key indicators of goodness-of-fit provided that chi-square = 983.45, with p-value for chi-square < 0.01, which indicates that the chi-square statistic is not closer to zero and the corresponding p-value is very small (significant), which is an indicator of weak fit. This indicates that the model is inadequate. However, RMSEA was estimated to be 0.01, CFI = 0.97, Non-normed fit index (NNFI) = 0.96 and NFI = 0.95. Hence, RMSEA, CFI and NFI assured for the model to be good fit. The chi-square probability for current repeated data indicated an unacceptable model fit (chi-square = 9.101, df = 3, p = 0.028).
The difference between observed and expected covariance structure matrices under current investigation was measured using chi-square value. The model under the study was also determined using a chi-square and the value was close to zero and corresponding probability value was greater than 0.05. Hence, the value for RMSEA was 0.123 which indicates that the model was unacceptable. On the other hand, the value for CFI was 0.998 and the corresponding value of NNFI was 0.988 which indicates that the model was acceptable to fit the data in current investigation with a value greater than 0.90 [13, 14].
Direct and Indirect effect of latent variables
The latent covariates, Socio-demographic, economic, individual, clinical factors, retention in HAART medication care and HAART adherence competence had direct and indirect effect on the CD4 cell count change. The direct and indirect effects are indicated in Table 2.
Table 2
Direct and indirect effect of latent covariates on CD4 cell count change
Effect
|
Direct effect
|
Indirect effect
|
Estimate
|
Standard error
|
Estimates
|
Standard errors
|
Socio-demographic variables
|
0.854
|
0.341
|
0.421
|
0.0321
|
Economic factors
|
0.642
|
0.453
|
0.405
|
0.4525
|
Individual characteristics
|
0.728
|
0.432
|
0.352
|
0.4216
|
Clinical factors
|
0.526
|
0.273
|
0.435
|
0.2723
|
HAART adherence competence
|
0.832
|
0.543
|
0.654
|
0.5434
|
Retention in HAART medication care
|
0.725
|
0.546
|
0.643
|
0.5463
|
Table 2 indicates that effect of latent variables which belongs to direct effect was a little bit greater than indirect effects. Hence, CD4 cell count change had been affected by direct and indirect involvement of latent variables.
Comparison of predictors of CD4 cell count change with and without latent variables
The comparison of approaches was conducted considering latent variables and without latent variables. Figure 1 indicates that, some of the variables are manifest and the others are latent. To compare approaches, the effect of observed variables without latent was compared with latent variables. The effect of manifest variables like level of education, marital status, sex of patients, disclosure of the disease to community living together, ownership of cell phone, level of income, weight, age, sex of patients, baseline CD4 cell count, WHO stages, adherence level and visiting times were potential predictors of CD4 cell count change without any latent variables using the same data and same study area. Here, it was possible to compare the effect of these manifest variables with and without latent variables, conducted previously by the same author, data and same study area[12, 15].
A baseline measurement and its change had different residual errors. The measurement model for each sex were verified and the result in the loadings and the fit indices were approximately similar while parameterization with and without equality of constraints.
A combination of repeated measurements was considered by the latent variable analysis and the result makes the latent variables vary together. Hence, the individual measurement analysis gave the same conclusion which was not achieved in the analysis conducted without latent variable approach. This implies that whenever, the individual analyses are not consistent, a latent variable model provides an easy interpretable synthesis.