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Background: Termination of pregnancy continues to be one of the major public health problems. The prevalence and determinants associated with pregnancy termination among women from low-middle income countries such as Tanzania have previously not been thoroughly investigated. Therefore, this study aims to explore the prevalence and determinants associated with pregnancy termination among Tanzanian women., Methods: Data for this study was extracted from the 2022 Tanzania Demographic and Health Survey (TDHS). A total of 15,254 women aged 15-49 years, clustered in selected enumerated areas, participated in the survey. A modified Poisson regression model with sampling weights was used to find the factors that are associated with pregnancy termination., Results: The prevalence of pregnancy termination was 14.3% (95% CI: 13.81-14.75%). Our findings reveal that several factors are significantly associated with pregnancy termination. In particular, women's age, level of education, marital status, history of pregnancy losses, wealth status, attempt to delay or avoid getting pregnant outside the ideal birth spacing, and recent internet use, total children ever born, desire for more children, were significantly associated with pregnancy termination. Women who reside in rural areas, those covered by health insurance and those using contraceptives had a significantly lower likelihood of terminating pregnancy compared to their counterparts., Conclusions: The findings highlighted that the prevalence of terminating pregnancy is alarming in Tanzania, signaling a significant public health challenge. To address the concerning rates of pregnancy termination, the government of Tanzania and other institutions are advised to enhance the accessibility and quality of healthcare services for women, particularly in rural and underserved areas. Bridging socio-economic inequalities and removing geographic barriers to healthcare access will ensure women receive timely and adequate support., Competing Interests: Declarations. Ethics approval and consent to participate: All methods concerning human participants in our study were conducted in accordance with the ethical standards laid out in the 1964 Declaration of Helsinki and its subsequent amendments. This study does not involve any experiment or interaction with human or animal subjects. The study uses secondary data from 2022 TDHS. The TDHS was reviewed and approved by the International Consulting and Fulfillment (ICF’s Inc.) Ethical Review Board. Researchers in this study obtained written permission from ICF to download and use the data. The dataset used in this study was anonymous. Consent for publication: Not applicable. Competing of interests: The authors declare no competing interests., (© 2025. The Author(s).)

CD4 count recovery is the main goal for an HIV patient who initiated ART. Early ART initiation in HIV patients can help restore immune function more effectively, even when they have reached an advanced stage. Some patients may respond positively to ART and attain CD4 count recovery. Meanwhile, other patients failing to recover their CD4 count due to non-adherence, treatment resistance and virological failure might lead to HIV-related complications and death. The purpose of this study was to find the determinants of death in patients who failed to recover their CD4 count after initiating antiretroviral therapy. The data used in this study was obtained from KwaZulu-Natal, South Africa, where 2528 HIV-infected patients with a baseline CD4 count of <200 cells/mm 3 were initiated on ART. We used a Fine-Gray sub-distribution hazard and cumulative incidence function to estimate potential confounding factors of death, where CD4 count recovery was a competing event for failure due to death. Patients who had no tuberculosis were 1.33 times at risk of dying before attaining CD4 count recovery [aSHR 1.33; 95% CI (0.96-1.85)] compared to those who had tuberculosis. Rural patients had a higher risk of not recovering and leading to death [aSHR 1.97; 95% CI (1.57-2.47)] than those from urban areas. The patient's tuberculosis status, viral load, regimen, baseline CD4 count, and location were significant contributors to death before CD4 count recovery. Intervention programs targeting HIV testing in rural areas for early ART initiation and promoting treatment adherence are recommended.

Background: SARS-CoV-2 (Covid-19 virus) infection exposed the unpreparedness of African countries to health-related issues, South Africa included. Africa recorded more than 211 853 deaths as a consequence of Covid-19. When rare and deadly diseases require urgent hospitalisation strikes, governments and healthcare providers are usually caught unprepared, resulting in huge loss of lives. Usually, at the beginning of such pandemics, there is no rich data for health practitioners and academics to be able to forecast the number of patients or deaths related to the pandemic. This study aims to predict the number of deaths associated with Covid-19 infection. With the availability of the number of deaths on a daily basis, the results stemming from this study are important to inform and plan health policy., Methods: This study uses the daily number of deaths due to Covid-19 infection. Exploratory data analysis reveals that the data exhibits non-normality, three structural breaks and volatility clustering characteristics. The Markov switching (MS)-generalized autoregressive conditional heteroscedasticity (GARCH)-type model combined with heavy-tailed distributions is fitted to the returns of the data. Using available daily reported Covid-19-related deaths up until 26 August 2021, we report 10-day ahead forecasts of deaths. All forecasts are compared to the actual observed values in the forecasting period., Results: The Anderson-Darling Goodness of fit test confirms that the fitted models are adequate for the data. The Kupiec likelihood ratio test and the root mean square error (RMSE) were used to select the robust model at different risk levels. At 95% the MS(3)-GARCH(1,1) combined with Pearson's type IV distribution (PIVD) is the best model. This indicates that the proposed best-fitting model is reasonable and can be used for predicting the daily number of deaths due to Covid-19., Conclusion: The MS(3)-GARCH(1,1)-PIVD model provides a reliable and accurate method for predicting the minimum number of death due to Covid-19. The accuracy of the proposed model will assist policymakers, academics and health practitioners in forecasting the volatility of future health-related deaths in which the predictability of volatility plays an integral role in health risk management., (© 2022. The Author(s).)