Application of random survival forests in understanding the determinants of under-five child mortality in Uganda in the presence of covariates that satisfy the proportional and non-proportional hazards assumption

Abstract Background Uganda just like any other Sub-Saharan African country, has a high under-five child mortality rate. To inform policy on intervention strategies, sound statistical methods are required to critically identify factors strongly associated with under-five child mortality rates. The Cox proportional hazards model has been a common choice in analysing data to understand factors strongly associated with high child mortality rates taking age as the time-to-event variable. However, due to its restrictive proportional hazards (PH) assumption, some covariates of interest which do not satisfy the assumption are often excluded in the analysis to avoid mis-specifying the model. Otherwise using covariates that clearly violate the assumption would mean invalid results. Methods Survival trees and random survival forests are increasingly becoming popular in analysing survival data particularly in the case of large survey data and could be attractive alternatives to models with the restrictive PH assumption. In this article, we adopt random survival forests which have never been used in understanding factors affecting under-five child mortality rates in Uganda using Demographic and Health Survey data. Thus the first part of the analysis is based on the use of the classical Cox PH model and the second part of the analysis is based on the use of random survival forests in the presence of covariates that do not necessarily satisfy the PH assumption. Results Random survival forests and the Cox proportional hazards model agree that the sex of the household head, sex of the child, number of births in the past 1 year are strongly associated to under-five child mortality in Uganda given all the three covariates satisfy the PH assumption. Random survival forests further demonstrated that covariates that were originally excluded from the earlier analysis due to violation of the PH assumption were important in explaining under-five child mortality rates. These covariates include the number of children under the age of five in a household, number of births in the past 5 years, wealth index, total number of children ever born and the child’s birth order. The results further indicated that the predictive performance for random survival forests built using covariates including those that violate the PH assumption was higher than that for random survival forests built using only covariates that satisfy the PH assumption. Conclusions Random survival forests are appealing methods in analysing public health data to understand factors strongly associated with under-five child mortality rates especially in the presence of covariates that violate the proportional hazards assumption

A comparison of the conditional inference survival forest model to random survival forests based on a simulation study as well as on two applications with time-to-event data

Abstract Background Random survival forest (RSF) models have been identified as alternative methods to the Cox proportional hazards model in analysing time-to-event data. These methods, however, have been criticised for the bias that results from favouring covariates with many split-points and hence conditional inference forests for time-to-event data have been suggested. Conditional inference forests (CIF) are known to correct the bias in RSF models by separating the procedure for the best covariate to split on from that of the best split point search for the selected covariate. Methods In this study, we compare the random survival forest model to the conditional inference model (CIF) using twenty-two simulated time-to-event datasets. We also analysed two real time-to-event datasets. The first dataset is based on the survival of children under-five years of age in Uganda and it consists of categorical covariates with most of them having more than two levels (many split-points). The second dataset is based on the survival of patients with extremely drug resistant tuberculosis (XDR TB) which consists of mainly categorical covariates with two levels (few split-points). Results The study findings indicate that the conditional inference forest model is superior to random survival forest models in analysing time-to-event data that consists of covariates with many split-points based on the values of the bootstrap cross-validated estimates for integrated Brier scores. However, conditional inference forests perform comparably similar to random survival forests models in analysing time-to-event data consisting of covariates with fewer split-points. Conclusion Although survival forests are promising methods in analysing time-to-event data, it is important to identify the best forest model for analysis based on the nature of covariates of the dataset in question

Prevalence, morphological characterization, and associated factors of anemia among children below 5 years of age attending St. Mary’s Hospital Lacor, Gulu District, Northern Uganda

Apollo Ocan,1 Caesar Oyet,1 Fred Webbo,1,2 Bashir Mwambi,1 Ivan Mugisha Taremwa1 1Institute of Allied Health Sciences, Clarke International University, Kampala, Uganda; 2Lancet Laboratories, Kampala, Uganda Aim/objective: The aim of this study was to determine the prevalence, severity, morphological characterization, and the associated factors of anemia among children under the age of 5 years at St. Mary’s Hospital Lacor, Gulu District, Northern Uganda.Materials and methods: A structured questionnaire was administered to each participant’s parent/caregiver to collect data on sociodemographic factors, feeding pattern, and history of chronic illness. Hemoglobin (Hb) estimation was performed using a HemoCue 201+ analyzer. Peripheral thin and thick blood films were made from venous blood and stained with Giemsa to morphologically characterize red blood cells (RBCs) and investigate hemoparasites, respectively. We collected and examined stool specimens from each participant using wet preparations and formol–ether concentration technique for intestinal parasites. Descriptive statistics was used to describe study participants and to determine the prevalence of anemia. Logistic regression analysis was done to determine the factors associated with acquiring anemia at a P-value≤ 0.05.Results: The study enrolled 343 children below the age of 5 years. Of these, 62.7% (N=215) were females. The IQR, median, and mean Hb levels were 5.1±3.2 g/dL, 8.2 g/dL, and 7.9 g/dL, respectively. Overall, 160 (46.6%, 95% CI: 42.1–51.46) children had anemia. The magnitude of severe, moderate, and mild anemia was 11.9%, 58.8%, and 29.4%, respectively. Morphologic characterization of anemia revealed hypochromic-microcytic (65.4%, N=106), hypochromic-macrocytic (15.4%, N=25), and normochromic-microcytic (19.1%, N=31) anemia. Factors associated with anemia were parasitic infestation, history of chronic disease, lack of complementary foods, complementary feeding for not more than twice a month, and households’ with annual income less than 200,000 Ugandan Shillings.Conclusion: We report the high prevalence of anemia among children below 5 years of age in Gulu District, Northern Uganda. Thus, strategies geared at addressing the etiologic causes (such as, nutrient deficiency and parasitic infections) are key to reduce it in the region. Keywords: anemia, associated factors, children below 5 years, Uganda&nbsp

Prevalence of RhD variants among blood donors at Gulu Regional Blood Bank, Gulu, Northern Uganda

Polycarp Ojok,1,2 Caesar Oyet,1 Fred Webbo,1,3 Bashir Mwambi,1 Ivan M Taremwa1 1Institute of Allied Health Sciences, International Health Sciences University, Kampala, 2Gulu Regional Blood Bank, Gulu, 3Lancet Laboratories, Kampala, Uganda Aim/objective: The aim of this study was to determine the prevalence of RhD variant ­phenotypes among voluntary non-remunerated blood donors (VNRBDs) at Gulu Regional Blood Bank (GRBB), Northern Uganda. Materials and methods: We conducted a cross-sectional study, in which the first 4.0 mL of ethylenediaminetetraacetic acid (EDTA) blood samples were collected from VNRBDs and typed for their ABO and RhD blood group status using IgM and IgG monoclonal typing antisera, respectively. Blood samples that tested as RhD negative were further investigated for RhD variant phenotypes using indirect antihuman globulin hemagglutination technique. Results: We assayed 138 RhD-negative blood samples obtained from VNRBDs. Of these, 66.7% (n=92) were males. Their median age was 24.4 years (range, 14–33 years). Majority of the participants were of ABO blood group O (62.8%, n=86), followed by A (19.7%, n=27), then B (13.9%, n=19) and least AB (3.6%, n=6). The prevalence of RhD variant phenotypes was 0.7% (n=1; 95% confidence interval, 0.5–0.9). There was no statistical association of RhD variant phenotypes with donor gender, tribe and their ABO blood groups. Conclusion: This study has revealed a high prevalence of RhD variant among blood donors at GRBB in Northern Uganda. It further highlights a potential risk of alloimmunization, as the present blood typing practices do not identify RhD variant phenotypes. Keywords: Rh blood group, D variants, D antigen, weak D, partial D, Uganda&nbsp

Assessment of the diagnostic performance of TrueHb® point-of-care hemometer compared with Sysmex i3 analyzer among patients at International Hospital Kampala, Uganda

Ivan Mugisha Taremwa,1 Ivan Ndeze,1 Bashir Mwambi,1 Christine Atuhairwe,2 Diana Inda Achieng,3 Bernard Natukunda41Department of Medical Laboratory Sciences, Institute of Allied Health Sciences, Clarke International University, Kampala, Uganda; 2Institute of Public Health and Management, Clarke International University, Kampala, Uganda; 3Lancet Laboratories, Kampala, Uganda; 4Department of  Medical Laboratory Sciences, Faculty of Medicine, Mbarara University of Science and Technology, Mbarara, UgandaAim/Objective: To assess the diagnostic performance of TrueHb® point-of-care (POC) hemometer compared with Sysmex i3 analyzer at International Hospital Kampala, Uganda.Materials and methods: We analyzed ethylenediaminetetraacetic acid blood samples to estimate hemoglobin (Hb) levels using parallel testing with TrueHb® hemometer and Sysmex i3 analyzer. Data were analyzed to ascertain the diagnostic performance of the test assays using the Bland and Altman method. Sensitivity, specificity, positive and negative predictive values were calculated.Results: The study enrolled 402 patients; of these, 156 (38.8%) were males. The average Hb levels were 8.7±1.8 and 13.3±2.6 g/dL for the anemic and nonanemic patients, respectively. One hundred and fifty-five participants were anemic, giving anemia prevalence of 38.56% (95% CI: 35.17–40.38). The mean difference of the TrueHb® and Sysmex i3 assays was 2.2219 (SD 1.07915), and the two devices did not show a difference in their measurements (t=−2.407, p-value 0.017, 95% CI: −0.095–0.010). Further, they showed a significant level of agreement (t=41.281; 95% CI: 2.1161–2.3277) and intraclass correlation coefficients (ICC=0.793). The sensitivity, specificity, positive and negative predictive values were 100.00%, 51.01%, 55.16% and 100.00%, respectively. The average performance turnaround time (TAT) for the TrueHb® hemometer was 2.46 mins (95% CI: 2.37–2.55).Conclusion: TrueHb® POC hemometer is an accurate POC for Hb estimation with a good performance agreement with the Sysmex i3 analyzer. This, coupled with its utility aspects, makes it a good diagnostic tool in a high anemia burden and low-resource setting.Keywords: anemia, hemoglobin estimation, point-of-care testing, TrueHb®, Sysmex i3, Ugand

Hematological abnormalities in HIV-antiretroviral therapy naïve clients as seen at an immune suppression syndrome clinic at Mbarara Regional Referral Hospital, southwestern Uganda

Crispus Katemba,1 Conrad Muzoora,2 Enoch Muwanguzi,1 Bashir Mwambi,3 Christine Atuhairwe,4 Ivan M Taremwa3 1Department of Medical Laboratory Sciences, Mbarara University of Science and Technology, Mbarara, Uganda; 2Department of Internal Medicine, Mbarara University of Science and Technology, Mbarara, Uganda; 3Institute of Allied Health Sciences, Clarke International University, Kampala, Uganda; 4Institute of Public Health and Management, Clarke International University, Kampala, Uganda Aim/objective: To assess the common hematological abnormalities among HIV-antiretroviral therapy (ART) naïve clients attending an immune suppression syndrome (ISS) clinic at Mbarara Regional Referral Hospital (MRRH), southwestern Uganda. Patients and methods: This was a cross-sectional study carried out during the months of March to August 2016 at the ISS clinic of MRRH. We collected approximately 4.0 mL of EDTA anticoagulated blood samples, which were assayed for complete blood count, CD4+ cell count and thin film examination. Correlation of the hematological abnormalities with CD4+ cell counts was done using correlation coefficient (r) and analysis of variance (F), and the p-value was set at ≤0.05. Results: A total of 141 clients were enrolled. Of these, 67.38% (95/141) were anemic, 26.24% (40/141) had thrombocytopenia while 26.95% (38/141) had leucopenia. Of the 95 participants with anemia, 89.47% (85/95) presented with normocytic-normochromic anemia, 8.42% (8/95) with microcytic-hypochromic anemia and 2.11% (2/95) with macrocytic-hypochromic anemia. Anemia was not different across the several World Health Organization (WHO) stages of HIV infection disease progression (p>0.05). Statistically significant differences were present among participants with leucopenia (p<0.05). Also, leucopenia was more prevalent (11/38) among participants in WHO stage 4 of HIV infection. CD4+ cell counts correlated with thrombocytopenia (r=0.24, p<0.05) and leucopenia (r=0.15, p<0.05). Conclusion: People living with HIV/AIDS (PLWHIV/AIDS) ought to be routinely monitored and treated for the occurrence of hematological abnormalities. Early initiation of ART can help to prevent some hematological abnormalities. Keywords: antiretroviral therapy, HIV, leucopenia, anemia, thrombocytopenia, Ugand

Predictors of singleton preterm birth using multinomial regression models accounting for missing data: A birth registry-based cohort study in northern Tanzania.

BackgroundPreterm birth is a significant contributor of under-five and newborn deaths globally. Recent estimates indicated that, Tanzania ranks the tenth country with the highest preterm birth rates in the world, and shares 2.2% of the global proportion of all preterm births. Previous studies applied binary regression models to determine predictors of preterm birth by collapsing gestational age at birth to MethodsWe carried out a secondary analysis of cohort data from the KCMC zonal referral hospital Medical Birth Registry for 44,117 women who gave birth to singletons between 2000-2015. KCMC is located in the Moshi Municipality, Kilimanjaro region, northern Tanzania. Data analysis was performed using Stata version 15.1. Assuming a nonmonotone pattern of missingness, data were imputed using a fully conditional specification (FCS) technique under the missing at random (MAR) assumption. Multinomial regression models with robust standard errors were used to determine predictors of moderately to late ([32,37) weeks of gestation) and very/extreme (ResultsThe overall proportion of preterm births among singleton births was 11.7%. The trends of preterm birth were significantly rising between the years 2000-2015 by 22.2% (95%CI 12.2%, 32.1%, pConclusionsThe trends of preterm birth have increased over time in northern Tanzania. Policy decisions should intensify efforts to improve maternal and child care throughout the course of pregnancy and childbirth towards preterm birth prevention. For a positive pregnancy outcome, interventions to increase uptake and quality of ANC services should also be strengthened in Tanzania at all levels of care, where several interventions can easily be delivered to pregnant women, especially those at high-risk of experiencing adverse pregnancy outcomes

Predictors of perinatal death in the presence of missing data: A birth registry-based study in northern Tanzania.

BACKGROUND:More than five million perinatal deaths occur each year globally. Despite efforts put forward during the millennium development goals era, perinatal deaths continue to increase relative to under-five deaths, especially in low- and middle-income countries. This study aimed to determine predictors of perinatal death in the presence of missing data using birth registry data from Kilimanjaro Christian Medical Center (KCMC), between 2000-2015. METHODS:This was a retrospective cohort study from the medical birth registry at KCMC referral hospital located in Moshi Municipality, Kilimanjaro region, northern Tanzania. Data were analyzed using Stata version 15.1. Multiple imputation by fully conditional specification (FCS) was used to impute missing values. Generalized estimating equations (GEE) were used to determine the marginal effects of covariates on perinatal death using a log link mean model with robust standard errors. An exchangeable correlation structure was used to account for the dependence of observations within mothers. RESULTS:Among 50,487 deliveries recorded in the KCMC medical birth registry between 2000-2015, 4.2% (95%CI 4.0%, 4.3%) ended in perinatal death (equivalent to a perinatal mortality rate (PMR) of 41.6 (95%CI 39.9, 43.3) deaths per 1,000 births). After the imputation of missing values, the proportion of perinatal death remained relatively the same. The risk of perinatal death was significantly higher among deliveries from mothers who resided in rural compared to urban areas (RR = 1.241, 95%CI 1.137, 1.355), with primary education level (RR = 1.201, 95%CI 1.083, 1.332) compared to higher education level, with <4 compared to ≥4 antenatal care (ANC) visits (RR = 1.250, 95%CI 1.146, 1.365), with postpartum hemorrhage (PPH) (RR = 2.638, 95%CI 1.997, 3.486), abruption placenta (RR = 4.218, 95%CI 3.438, 5.175), delivered a low birth weight baby (LBW) (RR = 4.210, 95%CI 3.788, 4.679), male child (RR = 1.090, 95%CI 1.007, 1.181), and were referred for delivery (RR = 2.108, 95%CI 1.919, 2.317). On the other hand, deliveries from mothers who experienced premature rupture of the membranes (PROM) (RR = 0.411, 95%CI 0.283, 0.598) and delivered through cesarean section (CS) (RR = 0.662, 95%CI 0.604, 0.724) had a lower risk of perinatal death. CONCLUSIONS:Perinatal mortality in this cohort is higher than the national estimate. Higher risk of perinatal death was associated with low maternal education level, rural residence, <4 ANC visits, PPH, abruption placenta, LBW delivery, child's sex, and being referred for delivery. Ignoring missing values in the analysis of adverse pregnancy outcomes produces biased covariate coefficients and standard errors. Close clinical follow-up of women at high risk of experiencing perinatal death, particularly during ANC visits and delivery, is of high importance to increase perinatal survival