Multilevel model on longitudinal data analysis in determinants of CD4 cell count among antiretroviral therapy attendant of HIV infected adults follow up in Gondar Teaching Referral Hospital, Gonder, Ethiopia

Predictors associated with CD4 cell count changes over time among HIV-infected children on anti-retroviral therapy follow-up in Mekelle General Hospital, Northern Ethiopia, 2019: a retrospective longitudinal study

INTRODUCTION

AIDS continues to be a serious global public health issue. It targets CD4 cells and immunological cells, which are in charge of the body's resistance against pathogenic pathogens. In situations with limited resources, CD4 cell measurement is essential for assessing treatment responses and clinical judgments in HIV-infected children receiving Anti-Retroviral Therapy (ART). The volatility of CD4 cells during ART follow-up is still largely uncharacterized, and there are few new datasets on CD4 cell changes over time. Therefore, the purpose of this analysis was to identify the factors that were predictive of CD4 cell count changes over time in children who started ART at Mekelle General Hospital in northern Ethiopia.

METHODS

A retrospective follow-up study was done. 437 patients in Mekelle general hospital, northern Ethiopia, from 2014-2016 were involved. All patients who have started anti-retrieval treatment (ART) and measured their CD4 cell count at least twice, including the baseline and those who initiated ART treatment, were included in the study population. An exploratory data analysis and linear mixed model analysis were used to explore the predictors of CD4 cell count change in patients and consider variability within and between patients.

RESULTS

This study found the correlation variation explained in cells accounted for between patients was 61.3%, and the remaining 38.7% variation existed within. This indicates that there is a substantial change in random slope and intercept between and within patients. WHO clinical stage IV (β = -1.30, 95% CI: -2.37, -0.23), co-infection HIV/TB (β = -1.78, 95% CI: -2.58, -0.98), children aged 2-5 (β = -0.43; 95% CI: -0.82, -0.04), and 6-14 years (β = -1.02; 95% CI: -1.47, -0.56), non-opportunistic infection (β = 1.33, 95% CI: 0.51, 2.14), and bedridden functional status (β = -1.74, 95% CI: -2.81, -0.68) were predictors of cell changes over time.

CONCLUSIONS

This study found that patients receiving ART experienced a significant change in CD4 cells over time. Because 61.3% of the variation in CD4 cells explained between patients and the remaining 38.7% within patients, such nested data structures are often strong correlation evidence. Co-infection of HIV/TB, functional status, age category of children, WHO clinical stage, and opportunistic infections are potential predictors of CD4 cells count change. Hence, special guidance and attention is also required, especially for those patients who have an opportunistic infections, higher WHO clinical stages, co-infections with HIV and TB, and bedridden functional status.

Multi-level analysis reveals the association between diabetes, body mass index, and HbA1c in an Iraqi population

Type 2 diabetes (T2D) known as a complex metabolic disorder may cause health problems and changes in blood biochemical markers. A growing number of studies have looked into several biomarkers and their connections with T2D risk. However, few have explored the interconnection of these biomarkers, as well as the prospective alterations in the diabetes biomarker correlation network. We conducted a secondary analysis in order to introduce a multi-level approach to establish a relationship between diabetes, pre-diabetes, blood biochemical markers, age, and body mass index (BMI). The dataset was obtained from the Mendeley Data (available at https://data.mendeley.com/datasets/wj9rwkp9c2/1 . In this study, three groups were established: non-diabetic (n = 103), pre-diabetic (n = 53), and diabetic (n = 844). According to the Heatmap analysis, non-diabetic and pre-diabetic individuals had the lowest BMI, age, and HbA1c. Diabetes and pre-diabetes were correlated with BMI (r = 0.58 and - 0.27, respectively), age (r = 0.47 and - 0.28, respectively), and HbA1c (r = 0.55 and - 0.21, respectively) using Pearson analysis. Using multivariate analysis, we found that diabetes, BMI, age, HbA1c, cholesterol, triglyceride, LDL, VLDL, and HDL were all associated. Network analysis revealed a connection between BMI and diabetes at the highest cut-off point. Moreover, receiver operating characteristic (ROC) analysis validated the network findings, revealing that BMI (area under the ROC curve, AUC = 0.95), HbA1c (AUC = 0.94), and age (AUC = 0.84) were the best predictors of diabetes. In conclusion, our multi-step study revealed that identifying significant T2D predictors, such as BMI and HbA1c, required a series of mathematical analyses.

The role of personality, social economic and prevention strategy effects on health-related quality of life among people living with HIV/AIDS

BACKGROUND

HIV/AIDS has transformed into a chronic controllable but not yet curable infectious disease as other chronic diseases to some extent. The additional of so called fourth 90% that included the improved health-related quality of life (HRQoL) for people living with HIV (PLWHIV) required solutions beyond antiretroviral therapy and viral load suppression. This study will explore the role of personality, social economic and prevention strategy effection on HRQoL among people living with HIV/AIDS.

METHODS

A cross-sectional study was conducted among PLWHIV aged more than 16 years old in the 10 municipalities in Yunnan Province, China from October 2019 to May 2020, enrolling total 1997 participants. Individual-level HRQoL data were measured by 12-item Short Form Health Survey (SF-12) and EuroQol Five Dimensions Questionnaire (EQ-5D-5L). We assembled municipal-level data about social economic from Yunnan Statistical Yearbook in 2020 and strategy practice information from the self-evaluation system. We used the principal component analysis to build the social economic and strategy effect on each area respectively and one-way ANOVA was used to perform univariate analysis to identify the predictors with significant differences. Finally we used multi-level model (MLM) to explore the personality, social economic and strategy effects in health-related quality of life among PLWHIV.

RESULTS

The global score for quality of life measured using EQ-5D-5L had an estimated mean score (standard deviation, SD) of 0.901 ± 0.146. The HRQoL score measured using PCS-12 had an estimated mean score (SD) of 46.62 ± 8.55. The mean MCS-12 score (SD) was estimated to be 47.80 ± 9.71. The area-level predictors explained a proportion of 13.6-17.2% for the between-area variation of the HRQoL scores, regardless of the total HRQoL, physical component and mental component. The impacts of stigma (P < 0.01), social support (P < 0.001), anxiety (P < 0.001), depression (P < 0.05) and social economic status (P < 0.05) on HRQoL at the individual-level were significantly different. The plots visualized the impact of individual-level factors on a respondent's HRQoL was modified by the area-level characteristics.

CONCLUSIONS

The study identified the possible strategy determinant of individual HRQoL of PLWHIV and also the area effect on HRQoL. Stigma, social support, anxiety, depression and social economic status were the individual-level determinants on HRQoL. These could be a valuable resource for evaluating the overall health of the areas and help improve local decision making.