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      Prediction of stillbirth low resource setting in Northern Uganda

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          Abstract

          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.

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          Most cited references22

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          National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: a systematic analysis

          Previous estimates have highlighted a large global burden of stillbirths, with an absence of reliable data from regions where most stillbirths occur. The Every Newborn Action Plan (ENAP) targets national stillbirth rates (SBRs) of 12 or fewer stillbirths per 1000 births by 2030. We estimate SBRs and numbers for 195 countries, including trends from 2000 to 2015.
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            Training and assessing classification rules with imbalanced data

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              A Low-Cost Ultrasound Program Leads to Increased Antenatal Clinic Visits and Attended Deliveries at a Health Care Clinic in Rural Uganda

              Background In June of 2010, an antenatal ultrasound program to perform basic screening for high-risk pregnancies was introduced at a community health care center in rural Uganda. Whether the addition of ultrasound scanning to antenatal visits at the health center would encourage or discourage potential patients was unknown. Our study sought to evaluate trends in the numbers of antenatal visits and deliveries at the clinic, pre- and post-introduction of antenatal ultrasound to determine what effect the presence of ultrasound at the clinic had on these metrics. Methods and Findings Records at Nawanyago clinic were reviewed to obtain the number of antenatal visits and deliveries for the 42 months preceding the introduction of ultrasound and the 23 months following. The monthly mean deliveries and antenatal visits by category (first visit through fourth return visit) were compared pre- and post- ultrasound using a Kruskal-Wallis one-way ANOVA. Following the introduction of ultrasound, significant increases were seen in the number of mean monthly deliveries and antenatal visits. The mean number of monthly deliveries at the clinic increased by 17.0 (13.3–20.6, 95% CI) from a pre-ultrasound average of 28.4 to a post-ultrasound monthly average of 45.4. The number of deliveries at a comparison clinic remained flat over this same time period. The monthly mean number of antenatal visits increased by 97.4 (83.3–111.5, 95% CI) from a baseline monthly average of 133.5 to a post-ultrasound monthly mean of 231.0, with increases seen in all categories of antenatal visits. Conclusions The availability of a low-cost antenatal ultrasound program may assist progress towards Millennium Development Goal 5 by encouraging women in a rural environment to come to a health care facility for skilled antenatal care and delivery assistance instead of utilizing more traditional methods.
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                Author and article information

                Contributors
                s.awor@gu.ac.ug
                Journal
                BMC Pregnancy Childbirth
                BMC Pregnancy Childbirth
                BMC Pregnancy and Childbirth
                BioMed Central (London )
                1471-2393
                19 November 2022
                19 November 2022
                2022
                : 22
                : 855
                Affiliations
                [1 ]GRID grid.442626.0, ISNI 0000 0001 0750 0866, Department of Obstetrics and Gynecology, , Faculty of Medicine Gulu University, ; Gulu, Uganda
                [2 ]GRID grid.416252.6, ISNI 0000 0000 9634 2734, Mulago National Referral Hospital, and Teaching Hospital for Makerere University, ; P.O.Box 7051, Kampala, Uganda
                [3 ]GRID grid.442626.0, ISNI 0000 0001 0750 0866, Department of Mathematics, , Faculty of Science, Gulu University, ; P.O.Box 166, Gulu, Uganda
                [4 ]GRID grid.11194.3c, ISNI 0000 0004 0620 0548, Department of Obstetrics and Gynaecology, , Makerere University, ; P.O.Box 7062, Kampala, Uganda
                [5 ]GRID grid.11194.3c, ISNI 0000 0004 0620 0548, Department of Community Health, School of Public Health, , College of Health Sciences Makerere University, ; P.O.Box 7062, Kampala, Uganda
                [6 ]Department of Pharmacology, School of Health Sciences, Lira University, P.O.Box 1035, Lira, Uganda
                Author information
                http://orcid.org/0000-0002-9701-2264
                Article
                5198
                10.1186/s12884-022-05198-6
                9675255
                36403017
                5b2f85ae-baf6-4653-a6c3-90ec735a3097
                © The Author(s) 2022

                Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

                History
                : 27 July 2022
                : 8 November 2022
                Categories
                Research
                Custom metadata
                © The Author(s) 2022

                Obstetrics & Gynecology
                stillbirth,risk factors,prediction models,uganda,africa
                Obstetrics & Gynecology
                stillbirth, risk factors, prediction models, uganda, africa

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