Background Pre-eclampsia is the second leading cause of maternal death in Uganda. However, mothers report to the hospitals late due to health care challenges. Therefore, we developed and validated the prediction models for prenatal screening for pre-eclampsia. Methods This was a prospective cohort study at St. Mary's hospital lacor in Gulu city. We included 1,004 pregnant mothers screened at 16–24 weeks (using maternal history, physical examination, uterine artery Doppler indices, and blood tests), followed up, and delivered. We built models in RStudio. Because the incidence of pre-eclampsia was low (4.3%), we generated synthetic balanced data using the ROSE (Random Over and under Sampling Examples) package in RStudio by over-sampling pre-eclampsia and under-sampling non-preeclampsia. As a result, we got 383 (48.8%) and 399 (51.2%) for pre-eclampsia and non-preeclampsia, respectively. Finally, we evaluated the actual model performance against the ROSE-derived synthetic dataset using K-fold cross-validation in RStudio. Results Maternal history of pre-eclampsia (adjusted odds ratio (aOR) = 32.75, 95% confidence intervals (CI) 6.59—182.05, p = 0.000), serum alkaline phosphatase(ALP) < 98 IU/L (aOR = 7.14, 95% CI 1.76—24.45, p = 0.003), diastolic hypertension ≥ 90 mmHg (aOR = 4.90, 95% CI 1.15—18.01, p = 0.022), bilateral end diastolic notch (aOR = 4.54, 95% CI 1.65—12.20, p = 0.003) and body mass index of ≥ 26.56 kg/m2 (aOR = 3.86, 95% CI 1.25—14.15, p = 0.027) were independent risk factors for pre-eclampsia. Maternal age ≥ 35 years (aOR = 3.88, 95% CI 0.94—15.44, p = 0.056), nulliparity (aOR = 4.25, 95% CI 1.08—20.18, p = 0.051) and white blood cell count ≥ 11,000 (aOR = 8.43, 95% CI 0.92—70.62, p = 0.050) may be risk factors for pre-eclampsia, and lymphocyte count of 800 – 4000 cells/microliter (aOR = 0.29, 95% CI 0.08—1.22, p = 0.074) may be protective against pre-eclampsia. A combination of all the above variables predicted pre-eclampsia with 77.0% accuracy, 80.4% sensitivity, 73.6% specificity, and 84.9% area under the curve (AUC). Conclusion The predictors of pre-eclampsia were maternal age ≥ 35 years, nulliparity, maternal history of pre-eclampsia, body mass index, diastolic pressure, white blood cell count, lymphocyte count, serum ALP and end-diastolic notch of the uterine arteries. This prediction model can predict pre-eclampsia in prenatal clinics with 77% accuracy.

Background Women of Afro-Caribbean and Asian origin are more at risk of stillbirths. However, there are limited tools built for risk-prediction models for stillbirth within sub-Saharan Africa. Therefore, we examined the predictors for stillbirth in low resource setting in Northern Uganda. Methods Prospective cohort study at St. Mary’s hospital Lacor in Northern Uganda. Using Yamane’s 1967 formula for calculating sample size for cohort studies using finite population size, the required sample size was 379 mothers. We doubled the number (to > 758) to cater for loss to follow up, miscarriages, and clients opting out of the study during the follow-up period. Recruited 1,285 pregnant mothers at 16–24 weeks, excluded those with lethal congenital anomalies diagnosed on ultrasound. Their history, physical findings, blood tests and uterine artery Doppler indices were taken, and the mothers were encouraged to continue with routine prenatal care until the time for delivery. While in the delivery ward, they were followed up in labour until delivery by the research team. The primary outcome was stillbirth 24 + weeks with no signs of life. Built models in RStudio. Since the data was imbalanced with low stillbirth rate, used ROSE package to over-sample stillbirths and under-sample live-births to balance the data. We cross-validated the models with the ROSE-derived data using K (10)-fold cross-validation and obtained the area under curve (AUC) with accuracy, sensitivity and specificity. Results The incidence of stillbirth was 2.5%. Predictors of stillbirth were history of abortion (aOR = 3.07, 95% CI 1.11—8.05, p = 0.0243), bilateral end-diastolic notch (aOR = 3.51, 95% CI 1.13—9.92, p = 0.0209), personal history of preeclampsia (aOR = 5.18, 95% CI 0.60—30.66, p = 0.0916), and haemoglobin 9.5 – 12.1 g/dL (aOR = 0.33, 95% CI 0.11—0.93, p = 0.0375). The models’ AUC was 75.0% with 68.1% accuracy, 69.1% sensitivity and 67.1% specificity. Conclusion Risk factors for stillbirth include history of abortion and bilateral end-diastolic notch, while haemoglobin of 9.5—12.1 g/dL is protective.

Objective: To examine predictors for stillbirth in low resource setting in Northern Uganda.Methods: Prospective cohort study at St. Mary’s hospital Lacor in Northern Uganda. Recruited 1,285 pregnant mothers at 16-24 weeks. Their history, physical findings, blood tests and uterine artery Doppler indices were taken, and the mothers followed up until delivery. Primary outcome was stillbirth (birth ≥24 weeks). Built models in RStudio. Since the data was imbalanced with low stillbirth rate, used ROSE package to over-sample stillbirths and under-sample live-births to balance the data. We cross-validated the models with the ROSE-derived data using K (10)-fold cross-validation and obtained the area under curve (AUC) with accuracy, sensitivity and specificity.Results: The incidence of stillbirth was 2.5%. Predictors of stillbirth were history of abortion, bilateral end-diastolic notch, personal history of preeclampsia, and haemoglobin 9.5 – 12.1g/dL. The models’ AUC was 75.0% with 68.1% accuracy, 69.1% sensitivity and 67.1% specificity.Conclusion: Risk factors for stillbirth include history of abortion (aOR = 3.07, 95% CI 1.11 - 8.05, p=0.0243) and bilateral end-diastolic notch (aOR = 3.51, 95% CI 1.13 - 9.92, p=0.0209), while haemoglobin of 9.5 - 12.1g/dL is protective (aOR = 0.33, 95% CI 0.11 - 0.93, p=0.0375).

Background: The SARS-CoV-2 virus that causes COVID-19 has severely impacted the health, economic and social status of Ugandans and people globally. Its mutations result into numerous deadly variants which are constantly emerging in short time intervals. The rise of these variants has led to increased virus transmissibility, disease severity and reduction in vaccine effectiveness. To-date, it is unclear which direction the pandemic is likely to take in the next few years.Methods: This study developed a deterministic two strain Susceptible-Exposed-Infectious-Recovered-Vaccinated (SEIR-V) mathematical model to describe the transmission dynamics of these SARS-CoV-2 variants. The model considers 10 compartments to deal with both strain one and strain two (emerging strain). Basic reproductive number R0 is computed and both local and global stability analysis of the disease free equilibrium investigated qualitatively. The model's bifurcation analysis revealed a forward type.Results: Simulation results indicate a 17.17 % increase in peak infections for strain two dominance as compared to strain one dominance. Further, a 10-fold increase in vaccination rate would lead to 16.98 % reduction in strain two peak infections. Furthermore, periodically administering booster doses within 18-24 months is recommended. Compulsory face mask usage and early hospitalization/isolation of symptomatic individuals would lead to 14.2 % and 16.98 % decrease in symptomatic infections respectively.Conclusions: Our results suggest that continuous vaccination (at a higher rate) of populations is still vital in the fight against COVID-19. Besides, results on face mask usage and hospitalization of symptomatic individuals provides valuable information that can be used to support emergency preparedness for future SARS-CoV-2 outbreaks.