https://eajahme.mzumbe.ac.tz/index.php/eajahme <p><strong>WELCOME TO THE EAJAHME<br /></strong></p> <p>The journal accepts original, high-quality articles focusing on applied experiences of monitoring and evaluation (M&amp;E) in the African health sector, with a particular focus on East Africa, as well as articles based on health-related M&amp;E experiences from outside of Africa to provide useful comparative lessons. All EAJAHME articles are freely available online.</p> Mzumbe University en-US eajahme 2591-6769 https://eajahme.mzumbe.ac.tz/index.php/eajahme/article/view/46 <p><strong>Introduction </strong></p> <p>Population surveys and demographic studies are the gold standard for estimating HIV prevalence. However, non-response in these surveys is of major concern, especially if it is not random and complete case analysis becomes an inappropriate data analysis method. Therefore, a comprehensive analysis that will account for the missing data must be used to obtain unbiased HIV prevalence estimates.</p> <p><strong>Methods</strong></p> <p>Serological samples were collected from participants who were residents of a Demographic Surveillance System (DSS) in Kisesa, Tanzania. HIV prevalence was estimated using three methods. Firstly, using the Complete case analysis (CCA), assuming data were Missing Completely at Random (MCAR). The other two methods, multiple imputations (MI) and inverse probability weighting (IPW) assumed that non-response was missing at random (MAR). For MI, a logistic regression model adjusting for age, sex, residence, and marital status was used to impute 20 datasets to re-estimate the HIV prevalence. The propensity for participating in the sero-survey and being tested for HIV given age, sex, residence, and marital status were generated using logistic regression models. Using the propensity scores, inverse probability weights were derived for participants who were tested for HIV.</p> <p><strong>Results</strong></p> <p>The overall CCA HIV prevalence estimate was 6.6% (95% CI: 6.0-7.2), with 5.4% (95% CI: 4.6-6.3) in males and 7.3% (95% CI: 6.6-8.1) in females. Using MI, the overall HIV prevalence was 6.8% (95% CI: 6.2-7.5), 6.2% (95% CI: 5.1-7.3) in males, and 7.4% (95% CI: 6.6-8.2) in females. Using IPW the overall HIV prevalence was 6.7% (95% CI: 6.1-7.4), with 5.5% (95% CI: 4.7-6.5) in males and 7.7% (95% CI: 7.0 - 8.6) in females. HIV prevalence differed significantly between age groups (p&lt;0.001), with the highest estimate in males aged 35-39 and females aged 40-44, and the lowest in both males and females aged 15-19 years.</p> <p><strong>Conclusion</strong></p> <p>Complete case analysis underestimates HIV prevalence compared to methods that adjust for missing data. After comparing CCA, MI, and IPW, we found out that the best method to adjust for missing data in population surveys is through the use of multiple imputations.&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;</p> TINASHE MHIKE JIM TODD MARK URASSA NEEMA R. MOSHA Copyright (c) 2023 eajahme 2023-10-23 2023-10-23 6 6 10.58498/eajahme.v6i6.46 https://eajahme.mzumbe.ac.tz/index.php/eajahme/article/view/47 <p><strong>Introduction </strong></p> <p>In Tanzania, self-referrals have been one of the sources of underutilization of primary-level health facilities resulting in the overutilization and overburdening of secondary-level health facilities. A significant number of patients receiving services at the outpatient departments in the secondary-level health facilities could have been served at the primary health facilities. Understanding of determinants of self-referral is critical to informing important interventions to improve the referral processes and rational utilization of health facilities at the two levels.&nbsp;&nbsp;</p> <p><strong>Methods</strong></p> <p>A cross-sectional study was conducted among 230 outpatients at Katavi Regional Referral Hospital in Tanzania. A convenient sampling method was used to enroll study participants. An interviewer-administered questionnaire was used to collect data. STATA version 15 was used to analyze data descriptively while the chi-square test was performed to establish the association between dependent and independent variables at a <em>p-</em>value of less than 0.05.</p> <p><strong>Results</strong></p> <p>The magnitude of self-referral was 66.5%. The perceived determinants of self-referral were quality of care offered (90.8 %), availability of medicine (89.4%), proximity of health facility (88.8%), and patient-perceived severity of the disease (86.7%). The availability of medicine (p= 0.015), quality of care offered (p=0.00), location of the facility (p=0.044), place of residence (p=0.04), and patient-perceived severity of disease (p=0.017) were statistically significantly associated with the self-referral practices.</p> <p><strong>Conclusion</strong></p> <p>The magnitude of self-referral practice was high. Availability of essential medicines, quality of care offered by the hospital, hospital proximity, patient-perceived severity of the disease, and availability of barriers to accessing healthcare in lower-level facilities were factors significantly associated with the self-referral practice. It is recommended that continuous investment should be made in lower-level facilities to ensure patients receive the care they need at every point of care.</p> MANYIZI MALALE BONIPHACE LYIMO HENRY A. MOLLEL GODFREY KACHOLI Copyright (c) 2023 eajahme 2023-10-23 2023-10-23 6 6 10.58498/eajahme.v6i6.47