Determinants of long term survival of patients initiated on HAART at the AIDS support organization, Uganda

Master of Public Health - MPHIt is well documented that mortality rates have decreased and the survival of HIV and AIDS patients has been prolonged since the introduction of highly active antiretroviral therapy (HAART) in 1996. Although HAART has dramatically improved the prognosis of HIV disease, some HIV patients on HAART still die of HIV related illnesses. It is important to understand what these factors are in order to mitigate the impact on these factors on patient survival and achieve better outcome for these patients. The aim of this study was to determine risk factors for long term survival of patients on HAART in Uganda. Data for 2,244 out of 30,000 clients receiving care and treatment at TASO Entebbe was retrospectively analyzed. TASO Entebbe is a non-governmental HIV clinic that provides care and treatment to HIV positive clients. Long term survival in this case was defined as survival for more than 5 years after initiation on HAART. Logistic regression and survival analysis were conducted. Female clients had a 12% lower risk of death compared to the male clients (AHR=0.88 [CI: 0.443- 0.936]). Clients that had pulmonary TB had 1.3 times higher risk of death compared to clients that did not have pulmonary TB (AHR=1.33 [CI: 1.162-2.733]). Clients initiated at CD4 cell counts less than 250 cells/μl had almost 7 times higher adjusted odds of death compared to those initiated at CD4 cell counts greater than 500 cells/μl (AOR= 6.95 [CI: 2.882-16.744]) and clients initiated at CD4 cell counts between 250 cells/μl and 500 cells/μl almost 3 times higher adjusted odds of death compared to clients initiated at CD4 cell counts greater than 500 cells/μl (AOR 2.56 [CI: 1.004-6.520]). It is recommended that an aggressive HIV testing strategy be put in place to facilitate early identification of HIV positive patients. Early identification would enable early initiation into HAART well before the CD4 cell counts fall below 500 cells/μl. The observed higher risk of mortality amongst men suggests interventions to promote early HIV testing and treatment initiation amongst men. The observed high risk of mortality for patients with pulmonary TB, calls for aggressive TB case finding and treatment of positive in order to reduce the HIV/TB related mortality

Model-based small area estimation methods and precise district-level HIV prevalence estimates in Uganda

Background Model-based small area estimation methods can help generate parameter estimates at the district level, where planned population survey sample sizes are not large enough to support direct estimates of HIV prevalence with adequate precision. We computed district-level HIV prevalence estimates and their 95% confidence intervals for districts in Uganda. Methods Our analysis used direct survey and model-based estimation methods, including Fay-Herriot (area-level) and Battese-Harter-Fuller (unit-level) small area models. We used regression analysis to assess for consistency in estimating HIV prevalence. We use a ratio analysis of the mean square error and the coefficient of variation of the estimates to evaluate precision. The models were applied to Uganda Population-Based HIV Impact Assessment 2016/2017 data with auxiliary information from the 2016 Lot Quality Assurance Sampling survey and antenatal care data from district health information system datasets for unit-level and area level models, respectively. Results Estimates from the model-based and the direct survey methods were similar. However, direct survey estimates were unstable compared with the model-based estimates. Area level model estimates were more stable than unit-level model estimates. The correlation between unit-level and direct survey estimates was (β1 = 0.66, r2 = 0.862), and correlation between area-level model and direct survey estimates was (β1 = 0.44, r2 = 0.698). The error associated with the estimates decreased by 37.5% and 33.1% for the unit-level and arealevel models, respectively, compared to the direct survey estimates. Conclusions Although the unit-level model estimates were less precise than the area-level model estimates, they were highly correlated with the direct survey estimates and had less standard error associated with estimates than the area-level model. Unit-level models provide more accurate and reliable data to support local decision-making when unit-level auxiliary information is available

Correcting for selection bias in HIV prevalence estimates: an application of sample selection models using data from population-based HIV surveys in seven sub-Saharan African countries.

INTRODUCTION: Population-based biomarker surveys are the gold standard for estimating HIV prevalence but are susceptible to substantial non-participation (up to 30%). Analytical missing data methods, including inverse-probability weighting (IPW) and multiple imputation (MI), are biased when data are missing-not-at-random, for example when people living with HIV more frequently decline participation. Heckman-type selection models can, under certain assumptions, recover unbiased prevalence estimates in such scenarios. METHODS: We pooled data from 142,706 participants aged 15-49 years from nationally representative cross-sectional Population-based HIV Impact Assessments in seven countries in sub-Saharan Africa, conducted between 2015 and 2018 in Tanzania, Uganda, Malawi, Zambia, Zimbabwe, Lesotho and Eswatini. We compared sex-stratified HIV prevalence estimates from unadjusted, IPW, MI and selection models, controlling for household and individual-level predictors of non-participation, and assessed the sensitivity of selection models to the copula function specifying the correlation between study participation and HIV status. RESULTS: In total, 84.1% of participants provided a blood sample to determine HIV serostatus (range: 76% in Malawi to 95% in Uganda). HIV prevalence estimates from selection models diverged from IPW and MI models by up to 5% in Lesotho, without substantial precision loss. In Tanzania, the IPW model yielded lower HIV prevalence estimates among males than the best-fitting copula selection model (3.8% vs. 7.9%). CONCLUSIONS: We demonstrate how HIV prevalence estimates from selection models can differ from those obtained under missing-at-random assumptions. Further benefits include exploration of plausible relationships between participation and outcome. While selection models require additional assumptions and careful specification, they are an important tool for triangulating prevalence estimates in surveys with substantial missing data due to non-participation

HIV, syphilis, and hepatitis B virus infection and male circumcision in five Sub-Saharan African countries: Findings from the Population-based HIV Impact Assessment surveys, 2015-2019.

Voluntary medical male circumcision (VMMC) has primarily been promoted for HIV prevention. Evidence also supports that male circumcision offers protection against other sexually transmitted infections. This analysis assessed the effect of circumcision on syphilis, hepatitis B virus (HBV) infection and HIV. Data from the 2015 to 2019 Population-based HIV Impact Assessments (PHIAs) surveys from Rwanda, Tanzania, Uganda, Zambia, and Zimbabwe were used for the analysis. The PHIA surveys are cross-sectional, nationally representative household surveys that include biomarking testing for HIV, syphilis and HBV infection. This is a secondary data analysis using publicly available PHIA data. Univariate and multivariable logistic regression models were created using pooled PHIA data across the five countries to assess the effect of male circumcision on HIV, active and ever syphilis, and HBV infection among sexually active males aged 15-59 years. Circumcised men had lower odds of syphilis infection, ever or active infection, and HIV, compared to uncircumcised men, after adjusting for covariates (active syphilis infection = 0.67 adjusted odds ratio (aOR), 95% confidence interval (CI), 0.52-0.87, ever having had a syphilis infection = 0.85 aOR, 95% CI, 0.73-0.98, and HIV = 0.53 aOR, 95% CI, 0.47-0.61). No difference between circumcised and uncircumcised men was identified for HBV infection (P = 0.75). Circumcised men have a reduced likelihood for syphilis and HIV compared to uncircumcised men. However, we found no statistically significant difference between circumcised and uncircumcised men for HBV infection