BackgroundMenarche age is an important indicator of reproductive health of a woman or a community. In industrial societies, age at menarche has been declining over the last 150 years with a secular trend, and similar trends have been reported in some developing countries. Menarche age is affected by genetic and environmental cues, including nutrition. The study was designed to determine the age at menarche and its relation to childhood critical life events and nutritional status in post-conflict northern Uganda.MethodsThis was a comparative cross-sectional study of rural and urban secondary school girls in northern Uganda. Structured questionnaires were administered to 274 secondary school girls, aged 12 – 18 years to determine the age at menarche in relation to home location, nutritional status, body composition and critical life events.ResultsThe mean age at menarche was 13.6 ± 1.3 for rural and 13.3 ± 1.4 years for urban dwelling girls (t = -1.996, p = 0.047). Among the body composition measures, hip circumference was negatively correlated with the age at menarche (r = -0.109, p = 0.036), whereas height, BMI and waist circumference did not correlate with menarche. Paternal (but not maternal) education was associated with earlier menarche (F = 2.959, p = 0.033). Childhood critical life events were not associated with age at menarche.ConclusionsAge at menarche differed among urban and rural dwelling school girls and dependent on current nutritional status, as manifested by the hip circumference. It was not associated with extreme stressful childhood critical life events.
BackgroundThe use of prescription medications without the involvement of medical professionals is a growing public health concern. Therefore this study was conducted to determine the prevalence of borrowing and sharing prescription medicines and associated socio-demographic factors among community members who had sought health care from COBERS health centres.MethodsWe conducted analytical cross – sectional study among former patients who sought treatment during the two months period prior to data collection in nine COBERS health centres. We used cluster proportional-to-size sampling method to get the numbers of research participants to be selected for interview from each COBERS site and logistic regression model was used to assess the associations.ResultsThe prevalence of borrowing prescription medication was found to be 35.9% (95% CI 33.5–38.2%) and sharing prescription medication was 32.7% (95% CI 30.4–34.9%). The Socio-demographic factors associated with borrowing prescription medicines were: age group ≤19 years (AOR = 2.64, 95%CI 1.47–4.74, p-value = 0.001); age group 20–29 years (AOR = 2.78, 95%CI 1.71–4.50, p-value≤0.001); age group 30–39 years (AOR = 1.90, 95%CI 1.18–3.06, p-value = 0.009); age group 40–49 (AOR = 1.83, 95%CI 1.15–2.92, p-value = 0.011); being a female (AOR = 2.01, 1.58–2.55, p-value< 0.001); being a Pentecostal by faith (AOR = 1.69, 95%CI 1.02–2.81, p-value = 0.042) and being Employed Salary Earner (AOR = 0.44, 95%CI 0.25–0.78, p-value = 0.005). The socio-demographic factors associated with sharing prescription medicines were: age group ≥19 years (AOR = 4.17, 95%CI 2.24–7.76, p-value< 0.001); age group 20–29 years (AOR = 3.91, 95%CI 2.46–6.29, p-value< 0.001); age group 30–39 years (AOR = 2.94, 95%CI 2.05–4.21, p-value< 0.001); age group 40–49 years (AOR = 2.22, 95%CI 1.29–3.82, p-value = 0.004); being female (AOR = 2.50, 95%CI 1.70–3.47, p-value< 0.001); being Pentecostal by faith (AOR = 2.15, 95%CI 1.15–4.03, p-value = 0.017); and being engaged in business (AOR = 1.80, 95%CI 1.16–2.80, p-value = 0.009).ConclusionA high proportion of study participants had borrowed or shared prescription medicines during the two months prior to our study. It is recommended that stakeholders sensitise the community members on the danger of borrowing and sharing prescription medicines to avert the practice.Electronic supplementary materialThe online version of this article (10.1186/s40360-018-0206-5) contains supplementary material, which is available to authorized users.
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).
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