Dataset Information

Under 10 mortality patterns, risk factors, and mechanisms in low resource settings of Eastern Uganda: An analysis of event history demographic and verbal social autopsy data.

ABSTRACT:

Background

Globally, the under-10 years of age mortality has not been comprehensively studied. We applied the life-course perspective in the analysis and interpretation of the event history demographic and verbal autopsy data to examine when and why children die before their 10th birthday.

Methods

We analysed a decade (2005-2015) of event histories data on 22385 and 1815 verbal autopsies data collected by Iganga-Mayuge HDSS in eastern Uganda. We used the lifetable for mortality estimates and patterns, and Royston-Parmar survival analysis approach for mortality risk factors' assessment.

Results

The under-10 and 5-9 years of age mortality probabilities were 129 (95% Confidence Interval [CI] = 123-370) per 1000 live births and 11 (95% CI = 7-26) per 1000 children aged 5-9 years, respectively. The top four causes of new-born mortality and stillbirth were antepartum maternal complications (31%), intrapartum-related causes including birth injury, asphyxia and obstructed labour (25%), Low Birth Weight (LBW) and prematurity (20%), and other unidentified perinatal mortality causes (18%). Malaria, protein deficiency including anaemia, diarrhoea or gastrointestinal, and acute respiratory infections were the major causes of mortality among those aged 0-9 years-contributing 88%, 88% and 46% of all causes of mortality for the post-neonatal, child and 5-9 years of age respectively. 33% of all causes of mortality among those aged 5-9 years was a share of Injuries (22%) and gastrointestinal (11%). Regarding the deterministic pattern, nearly 30% of the new-borns and sick children died without access to formal care. Access to the treatment for the top five morbidities was after 4 days of symptoms' recognition. The childhood mortality risk factors were LBW, multiple births, having no partner, adolescence age, rural residence, low education level and belonging to a poor household, but their association was stronger among infants.

Conclusions

We have identified the vulnerable groups at risk of mortality as LBW children, multiple births, rural dwellers, those whose mother are of low socio-economic position, adolescents and unmarried. The differences in causes of mortalities between children aged 0-5 and 5-9 years were noted. These findings suggest for a strong life-course approach in the design and implementation of child health interventions that target pregnant women and children of all ages.

SUBMITTER: Kananura RM

PROVIDER: S-EPMC7289412 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Publications

Under 10 mortality patterns, risk factors, and mechanisms in low resource settings of Eastern Uganda: An analysis of event history demographic and verbal social autopsy data.

Kananura Rornald Muhumuza RM Leone Tiziana T Nareeba Tryphena T Kajungu Dan D Waiswa Peter P Gjonca Arjan A

PloS one 20200611 6

<h4>Background</h4>Globally, the under-10 years of age mortality has not been comprehensively studied. We applied the life-course perspective in the analysis and interpretation of the event history demographic and verbal autopsy data to examine when and why children die before their 10th birthday.<h4>Methods</h4>We analysed a decade (2005-2015) of event histories data on 22385 and 1815 verbal autopsies data collected by Iganga-Mayuge HDSS in eastern Uganda. We used the lifetable for mortality es ...[more]

PMID: 32525931

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Project description:BackgroundVerbal Autopsy (VA) is widely viewed as the only immediate strategy for registering cause of death in much of Africa and Asia, where routine physician certification of deaths is not widely practiced. VA involves a lay interview with family or friends after a death, to record essential details of the circumstances. These data can then be processed automatically to arrive at standardized cause of death information.MethodsThe Population Health Metrics Research Consortium (PHMRC) undertook a study at six tertiary hospitals in low- and middle-income countries which documented over 12,000 deaths clinically and subsequently undertook VA interviews. This dataset, now in the public domain, was compared with the WHO 2012 VA standard and the InterVA-4 interpretative model.ResultsThe PHMRC data covered 70% of the WHO 2012 VA input indicators, and categorized cause of death according to PHMRC definitions. After eliminating some problematic or incomplete records, 11,984 VAs were compared. Some of the PHMRC cause definitions, such as 'preterm delivery', differed substantially from the International Classification of Diseases, version 10 equivalent. There were some appreciable inconsistencies between the hospital and VA data, including 20% of the hospital maternal deaths being described as non-pregnant in the VA data. A high proportion of VA cases (66%) reported respiratory symptoms, but only 18% of assigned hospital causes were respiratory-related. Despite these issues, the concordance correlation coefficient between hospital and InterVA-4 cause of death categories was 0.61.ConclusionsThe PHMRC dataset is a valuable reference source for VA methods, but has to be interpreted with care. Inherently inconsistent cases should not be included when using these data to build other VA models. Conversely, models built from these data should be independently evaluated. It is important to distinguish between the internal and external validity of VA models. The effects of using tertiary hospital data, rather than the more usual application of VA to all-community deaths, are hard to evaluate. However, it would still be of value for VA method development to have further studies of population-based post-mortem examinations.

Project description:BackgroundThe most common method for determining cause of death is certification by physicians based either on available medical records, or where such data are not available, through verbal autopsy (VA). The physician-certification approach is costly and inconvenient; however, recent work shows the potential of a computer-based probabilistic model (InterVA) to interpret verbal autopsy data in a more convenient, consistent, and rapid way. In this study we validate separately both physician-certified verbal autopsy (PCVA) and the InterVA probabilistic model against hospital cause of death (HCOD) in adults dying in a district hospital on the coast of Kenya.MethodsBetween March 2007 and June 2010, VA interviews were conducted for 145 adult deaths that occurred at Kilifi District Hospital. The VA data were reviewed by a physician and the cause of death established. A range of indicators (including age, gender, physical signs and symptoms, pregnancy status, medical history, and the circumstances of death) from the VA forms were included in the InterVA for interpretation. Cause-specific mortality fractions (CSMF), Cohen's kappa (κ) statistic, receiver operating characteristic (ROC) curves, sensitivity, specificity, and positive predictive values were applied to compare agreement between PCVA, InterVA, and HCOD.ResultsHCOD, InterVA, and PCVA yielded the same top five underlying causes of adult deaths. The InterVA overestimated tuberculosis as a cause of death compared to the HCOD. On the other hand, PCVA overestimated diabetes. Overall, CSMF for the five major cause groups by the InterVA, PCVA, and HCOD were 70%, 65%, and 60%, respectively. PCVA versus HCOD yielded a higher kappa value (κ = 0.52, 95% confidence interval [CI]: 0.48, 0.54) than the InterVA versus HCOD which yielded a kappa (κ) value of 0.32 (95% CI: 0.30, 0.38). Overall, (κ) agreement across the three methods was 0.41 (95% CI: 0.37, 0.48). The areas under the ROC curves were 0.82 for InterVA and 0.88 for PCVA. The observed sensitivities and specificities across the five major causes of death varied from 43% to 100% and 87% to 99%, respectively, for the InterVA/PCVA against the HCOD.ConclusionBoth the InterVA and PCVA compared well with the HCOD at a population level and determined the top five underlying causes of death in the rural community of Kilifi. We hope that our study, albeit small, provides new and useful data that will stimulate further definitive work on methods of interpreting VA data.

Project description:BackgroundThe South African national cause of death validation (NCODV 2017/18) project collected a national sample of verbal autopsies (VA) with cause of death (COD) assignment by physician-coded VA (PCVA) and computer-coded VA (CCVA).ObjectiveThe performance of three CCVA algorithms (InterVA-5, InSilicoVA and Tariff 2.0) in assigning a COD was compared with PCVA (reference standard).MethodsSeven performance metrics assessed individual and population level agreement of COD assignment by age, sex and place of death subgroups. Positive predictive value (PPV), sensitivity, overall agreement, kappa, and chance corrected concordance (CCC) assessed individual level agreement. Cause-specific mortality fraction (CSMF) accuracy and Spearman's rank correlation assessed population level agreement.ResultsA total of 5386 VA records were analysed. PCVA and CCVAs all identified HIV/AIDS as the leading COD. CCVA PPV and sensitivity, based on confidence intervals, were comparable except for HIV/AIDS, TB, maternal, diabetes mellitus, other cancers, and some injuries. CCVAs performed well for identifying perinatal deaths, road traffic accidents, suicide and homicide but poorly for pneumonia, other infectious diseases and renal failure. Overall agreement between CCVAs and PCVA for the top single cause (48.2-51.6) indicated comparable weak agreement between methods. Overall agreement, for the top three causes showed moderate agreement for InterVA (70.9) and InSilicoVA (73.8). Agreement based on kappa (-0.05-0.49)and CCC (0.06-0.43) was weak to none for all algorithms and groups. CCVAs had moderate to strong agreement for CSMF accuracy, with InterVA-5 highest for neonates (0.90), Tariff 2.0 highest for adults (0.89) and males (0.84), and InSilicoVA highest for females (0.88), elders (0.83) and out-of-facility deaths (0.85). Rank correlation indicated moderate agreement for adults (0.75-0.79).ConclusionsWhilst CCVAs identified HIV/AIDS as the leading COD, consistent with PCVA, there is scope for improving the algorithms for use in South Africa.

Project description:BackgroundTo accelerate progress toward the Millennium Development Goal 4, reliable information on causes of child mortality is critical. With more national verbal autopsy (VA) studies becoming available, how to improve consistency of national VA derived child causes of death should be considered for the purpose of global comparison. We aimed to adapt a standardized computer algorithm to re-analyze national child VA studies conducted in Uganda, Rwanda and Ghana recently, and compare our results with those derived from physician review to explore issues surrounding the application of the standardized algorithm in place of physician review.Methods and findingsWe adapted the standardized computer algorithm considering the disease profile in Uganda, Rwanda and Ghana. We then derived cause-specific mortality fractions applying the adapted algorithm and compared the results with those ascertained by physician review by examining the individual- and population-level agreement. Our results showed that the leading causes of child mortality in Uganda, Rwanda and Ghana were pneumonia (16.5-21.1%) and malaria (16.8-25.6%) among children below five years and intrapartum-related complications (6.4-10.7%) and preterm birth complications (4.5-6.3%) among neonates. The individual level agreement was poor to substantial across causes (kappa statistics: -0.03 to 0.83), with moderate to substantial agreement observed for injury, congenital malformation, preterm birth complications, malaria and measles. At the population level, despite fairly different cause-specific mortality fractions, the ranking of the leading causes was largely similar.ConclusionsThe standardized computer algorithm produced internally consistent distribution of causes of child mortality. The results were also qualitatively comparable to those based on physician review from the perspective of public health policy. The standardized computer algorithm has the advantage of requiring minimal resources from the health care system and represents a promising way to re-analyze national or sub-national VA studies in place of physician review for the purpose of global comparison.

Project description:BackgroundNigeria has one of the highest under-five mortality rates in the world. Identifying the causes of these deaths is crucial to inform changes in policy documents, design and implementation of appropriate interventions to reduce these deaths. This study aimed to provide national and zonal-level estimates of the causes of under-five death in Nigeria in the 2013-2018 periods.MethodsWe conducted retrospective inquiries into the cause of deaths of 948 neonates and 2,127 children aged 1-59 months as identified in the 2018 Nigeria Demographic and Health Survey (NDHS). The verbal autopsy asked about signs and symptoms during the final illness. The Physician Coded Verbal Autopsy (PCVA) and Expert Algorithm Verbal Autopsy (EAVA) methods were employed to assign the immediate and underlying cause of deaths to all cases.ResultFor the analysis, sampling weights were applied to accommodate non-proportional allocation. Boys accounted for 56 percent of neonatal deaths and 51.5 percent of the 1-59-months old deaths. About one-quarter of under-5 mortality was attributed to neonatal deaths, and 50 percent of these neonatal deaths were recorded within 48 h of delivery. Overall, 84 percent of the under-5 deaths were in the northern geopolitical zones. Based on the two methods for case analysis, neonatal infections (sepsis, pneumonia, and meningitis) were responsible for 44 percent of the neonatal deaths, followed by intrapartum injury (PCVA: 21 percent vs. EAVA: 29 percent). The three main causes of death in children aged 1-59 months were malaria (PCVA: 23 percent vs. EAVA: 35 percent), diarrhoea (PCVA: 17 percent vs. EAVA: 23 percent), and pneumonia (PCVA: 10 percent vs. EAVA: 12 percent). In the North West, where the majority of under-5 (1-59 months) deaths were recorded, diarrhoea was the main cause of death (PCVA: 24.3 percent vs. EAVA: 30 percent).ConclusionThe causes of neonatal and children aged 1-59 months deaths vary across the northern and southern regions. By homing on the specific causes of mortality by region, the study provides crucial information that may be useful in planning appropriately tailored interventions to significantly reduce under-five deaths in Nigeria.

Dataset Information

Under 10 mortality patterns, risk factors, and mechanisms in low resource settings of Eastern Uganda: An analysis of event history demographic and verbal social autopsy data.

Background

Methods

Results

Conclusions

Publications

Under 10 mortality patterns, risk factors, and mechanisms in low resource settings of Eastern Uganda: An analysis of event history demographic and verbal social autopsy data.

Similar Datasets

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