Prevalence and risk factors of type-2 diabetes mellitus in Ethiopia: systematic review and meta-analysis.

Diabetes mellitus (DM) is a public health problem in developing as well as developed nations. DM leads to many complications that are associated with higher morbidity and mortality worldwide. Therefore, the current study was planned to assess the prevalence and risk factors of type-2 DM in Ethiopian population. Six electronic databases such as: PubMed, Scopus, Hinari, Web of science, Google Scholar, and African Journals Online were searched for studies published in English up December 30, 2020. Newcastle-Ottawa Scale was used for quality assessment of the included studies. The data was extracted by Microsoft excel and analyzed through Stata version 16 software. The random effect meta-regression analysis was computed at 95% CI to assess the pooled prevalence and risk factors of type-2 DM. Forty observational studies were included in this systematic review and meta-analysis. The pooled prevalence of DM in Ethiopia was 6.5% (95% CI (5.8, 7.3)). The sub-group analysis revealed that the highest prevalence of DM was found in Dire Dawa city administration (14%), and the lowest prevalence was observed in Tigray region (2%). The pooled prevalence of DM was higher (8%) in studies conducted in health facility. Factors like: Age ≥ 40 years ((Adjusted Odds Ratio (AOR): 1.91 (95% CI: 1.05, 3.49)), Illiterate (AOR: 2.74 (95% CI: 1.18, 6.34)), Cigarette smoking (AOR: 1.97 (95% CI: 1.17, 3.32)), Body mass index (BMI) ≥ 25 kg/m2 (AOR: 2.01 (95 CI: 1.46, 2.27)), family history of DM (AOR: 6.14 (95% CI: 2.80, 13.46)), history of hypertension (AOR: 3.00 (95% CI: 1.13, 7.95)) and physical inactivity (AOR: 5.79 (95% CI: 2.12, 15.77)) were significantly associated with type-2 DM in Ethiopian population. In this review, the prevalence of type-2 DM was high. Factors like: Older age, illiteracy, cigarette smoking, MBI ≥ 25, family history of DM, history of hypertension and physical inactivity were an identified risk factors of type-2 DM. Therefore, health education and promotion will be warranted. Further, large scale prospective studies will be recommended to address possible risk factors of type-2 DM in Ethiopian population.

Joint Modeling in Detecting Predictors of CD4 Cell Count and Status of Tuberculosis Among People Living with HIV/AIDS Under HAART at Felege Hiwot Teaching and Specialized Hospital, North-West Ethiopia.

BACKGROUND: Globally, for individuals infected with HIV, the presence of other infections including TB tends to increase the rate of HIV replication. Of the 8.8 million TB cases worldwide, an estimated 1.1 million (13%) were found to be co-infected with HIV. This research was conducted with the objective to identify potential predictors for the status of TB and CD4 cell count under PLWHIV at Felege Hiwot Specialized Hospital, North-west Ethiopia. METHODS: A retrospective repeated measurement was taken from a sample of 226 HIV patients. Separate and joint models were conducted for data analysis of CD4 cell count and TB status of people living with HIV. RESULTS: The descriptive statistics indicated that among the HIV patients receiving HAART, 26.6% had additional TB. AIDS clinical stage, weight, and hemoglobin level had a significant positive association with CD4 cell count, but a negative association with TB status. Weight and CD4 cell count have a negative relationship with the event of HIV/TB co-infection. Hence, the expected number of CD4 cell count of HIV patients who were co-infected with TB was decreased by 2.34 as compared to people living with HIV without TB. As visiting times of patients to hospitals for treatment increased by one unit, the odds of being co-infected with TB was decreased by 0.05, and the expected number of CD4 cell count was increased by 0.2. As patients' age increased by one year, the expected number of CD4 cell count was decreased by 0.025 cells per/mm3. CONCLUSION: Having lower CD4 cell count, lower weight, late WHO clinical stage, being non-adherent, having opportunistic infection, having lower hemoglobin, being ambulatory and bedridden were associated with a higher risk of co-infection of HIV/TB and were indicators of progression of the disease.

Joint longitudinal data analysis in detecting determinants of CD4 cell count change and adherence to highly active antiretroviral therapy at Felege Hiwot Teaching and Specialized Hospital, North-west Ethiopia (Amhara Region).

BACKGROUND: Adherence and CD4 cell count change measure the progression of the disease in HIV patients after the commencement of HAART. Lack of information about associated factors on adherence to HAART and CD4 cell count reduction is a challenge for the improvement of cells in HIV positive adults. The main objective of adopting joint modeling was to compare separate and joint models of longitudinal repeated measures in identifying long-term predictors of the two longitudinal outcomes: CD4 cell count and adherence to HAART. METHODS: A longitudinal retrospective cohort study was conducted to examine the joint predictors of CD4 cell count change and adherence to HAART among HIV adult patients enrolled in the first 10 months of the year 2008 and followed-up to June 2012. Joint model was employed to determine joint predictors of two longitudinal response variables over time. Furthermore, the generalized linear mixed effect model had been used for specification of the marginal distribution, conditional to correlated random effect. RESULTS: A total of 792 adult HIV patients were studied to analyze the longitudinal joint model study. The result from this investigation revealed that age, weight, baseline CD4 cell count, ownership of cell phone, visiting times, marital status, residence area and level of disclosure of the disease to family members had significantly affected both outcomes. From the two-way interactions, time * owner of cell phone, time * sex, age * sex, age * level of education as well as time * level of education were significant for CD4 cell count change in the longitudinal data analysis. The multivariate joint model with linear predictor indicates that CD4 cell count change was positively correlated (p ≤ 0.0001) with adherence to HAART. Hence, as adherence to HAART increased, CD4 cell count also increased; and those patients who had significant CD4 cell count change at each visiting time had been encouraged to be good adherents. CONCLUSION: Joint model analysis was more parsimonious as compared to separate analysis, as it reduces type I error and subject-specific analysis improved its model fit. The joint model operates multivariate analysis simultaneously; and it has great power in parameter estimation. Developing joint model helps validate the observed correlation between the outcomes that have emerged from the association of intercepts. There should be a special attention and intervention for HIV positive adults, especially for those who had poor adherence and with low CD4 cell count change. The intervention may be important for pre-treatment counseling and awareness creation. The study also identified a group of patients who were with maximum risk of CD4 cell count change. It is suggested that this group of patients needs high intervention for counseling.

Quasi-Poisson versus negative binomial regression models in identifying factors affecting initial CD4 cell count change due to antiretroviral therapy administered to HIV-positive adults in North-West Ethiopia (Amhara region).

BACKGROUND: CD4 cells are a type of white blood cells that plays a significant role in protecting humans from infectious diseases. Lack of information on associated factors on CD4 cell count reduction is an obstacle for improvement of cells in HIV positive adults. Therefore, the main objective of this study was to investigate baseline factors that could affect initial CD4 cell count change after highly active antiretroviral therapy had been given to adult patients in North West Ethiopia. METHODS: A retrospective cross-sectional study was conducted among 792 HIV positive adult patients who already started antiretroviral therapy for 1 month of therapy. A Chi square test of association was used to assess of predictor covariates on the variable of interest. Data was secondary source and modeled using generalized linear models, especially Quasi-Poisson regression. RESULTS: The patients' CD4 cell count changed within a month ranged from 0 to 109 cells/mm3 with a mean of 15.9 cells/mm3 and standard deviation 18.44 cells/mm3. The first month CD4 cell count change was significantly affected by poor adherence to highly active antiretroviral therapy (aRR = 0.506, P value = 2e-16), fair adherence (aRR = 0.592, P value = 0.0120), initial CD4 cell count (aRR = 1.0212, P value = 1.54e-15), low household income (aRR = 0.63, P value = 0.671e-14), middle income (aRR = 0.74, P value = 0.629e-12), patients without cell phone (aRR = 0.67, P value = 0.615e-16), WHO stage 2 (aRR = 0.91, P value = 0.0078), WHO stage 3 (aRR = 0.91, P value = 0.0058), WHO stage 4 (0876, P value = 0.0214), age (aRR = 0.987, P value = 0.000) and weight (aRR = 1.0216, P value = 3.98e-14). CONCLUSIONS: Adherence to antiretroviral therapy, initial CD4 cell count, household income, WHO stages, age, weight and owner of cell phone played a major role for the variation of CD4 cell count in our data. Hence, we recommend a close follow-up of patients to adhere the prescribed medication for achievements of CD4 cell count change progression.