Mortality Prevention Surveillance Network Research Articles

Impact of anthropometry training and feasibility of 3D imaging on anthropometry data quality among children under five years in a postmortem setting.

The Child Health and Mortality Prevention Surveillance Network (CHAMPS) identifies causes of under-5 mortality in high mortality countries. To address challenges in postmortem nutritional assessment, we evaluated the impact of anthropometry training and the feasibility of 3D imaging on data quality within the CHAMPS Kenya site. Staff were trained using World Health Organization (WHO)-recommended manual anthropometry equipment and novel 3D imaging methods to collect postmortem measurements. Following training, 76 deceased children were measured in duplicate and were compared to measurements of 75 pre-training deceased children. Outcomes included measures of data quality (standard deviations of anthropometric indices and digit preference scores (DPS)), precision (absolute and relative technical errors of measurement, TEMs or rTEMs), and accuracy (Bland-Altman plots). WHO growth standards were used to produce anthropometric indices. Post-training surveys and in-depth interviews collected qualitative feedback on measurer experience with performing manual anthropometry and ease of using 3D imaging software. Manual anthropometry data quality improved after training, as indicated by DPS. Standard deviations of anthropometric indices exceeded limits for high data quality when using the WHO growth standards. Reliability of measurements post-training was high as indicated by rTEMs below 1.5%. 3D imaging was highly correlated with manual measurements; however, on average 3D scans overestimated length and head circumference by 1.61 cm and 2.27 cm, respectively. Site staff preferred manual anthropometry to 3D imaging, as the imaging technology required adequate lighting and additional considerations when performing the measurements. Manual anthropometry was feasible and reliable postmortem in the presence of rigor mortis. 3D imaging may be an accurate alternative to manual anthropometry, but technology adjustments are needed to ensure accuracy and usability.

Prevalence and Missed Cases of Respiratory Distress Syndrome Disease Amongst Neonatal Deaths Enrolled in the Kenya Child Health and Mortality Prevention Surveillance Network (CHAMPS) Program Between 2017 and 2021

Objectives. To describe RDS in neonatal deaths at the CHAMPS-Kenya site between 2017 and 2021. Methods. We included 165 neonatal deaths whose their Causes of death (COD) were determined by a panel of experts using data from post-mortem conducted through minimally invasive tissue specimen testing, clinical records, and verbal autopsy. Results. Twenty-six percent (43/165) of neonatal deaths were attributable to RDS. Most cases occurred in low birthweight and preterm neonates. From these cases, less than half of the hospitalizations were diagnosed with RDS before death, and essential diagnostic tests were not performed in most cases. Most cases received suboptimal levels of supplemental oxygen, and critical interventions like surfactant replacement therapy and mechanical ventilation were not adequately utilized when available. Conclusion. The study highlights the urgent need for improved diagnosis and management of RDS, emphasizing the importance of increasing clinical suspicion and enhancing training in its clinical management to reduce mortality rates.

Using 3D Imaging Technology to Accurately Collect Postmortem Anthropometric Measurements in Children Under 5 Years of Age

Abstract Objectives The Child Health and Mortality Prevention Surveillance Network (CHAMPS) aims to identify causes of under-5 mortality in sub-Saharan African and South Asian surveillance sites. To address challenges in postmortem nutritional assessment, we evaluated anthropometry training and 3D imaging in the CHAMPS Kenya site. Methods Staff were trained using World Health Organization (WHO) recommended standard anthropometry equipment as well as 3D imaging to collect postmortem measurements. Following the training, 76 cases were measured in duplicate using standard anthropometry and 3D imaging and were compared to 75 pre-intervention cases. Outcomes included data quality metrics [standard deviations (SD), digit preference, % biologically implausible values (BIV, Length-for-age z-score (LAZ) BIV = ± 6 SD), measurement reliability (technical errors of measurement, TEM), and accuracy (correlation coefficients and Bland Altman plots of standard vs. 3D scan measurements). We used both WHO growth standard and internal standardization to produce sex and age-specific LAZ. Results Standard anthropometry data quality improved as indicated by digit preference (all measures rounded to 0.0 or 0.5 pre-intervention vs. no preference post-intervention). When using the WHO growth standards, we observed increases between pre- and post-training LAZ SD (2.55 vs. 2.92) and % BIV (5.33 vs. 15.13). Internal standardization eliminated the % BIV, with pre-intervention LAZ ranging from-1.78 to 2.27, and post intervention LAZ: −2.27 to 2.04, falling within the WHO ranges for biologically plausible values (−6 SD < LAZ < 6 SD). Reliability of length measurements post-intervention was high as indicated by low relative TEM of 0.53%. Accuracy of 3D imaging was high (R = 0.99) comparing post-training vs. 3D imaging for length; however, examination of Bland Altman plots revealed that on average 3D scans overestimated length by 3.87 centimeters. Conclusions Training on standard anthropometry improved data quality. 3D imaging may be an accurate alternative to standard anthropometry, but adjustment of the technology is needed to avoid overestimation of length. Future research on the appropriate use of reference standards to define malnutrition in this severely ill population is needed. Funding Sources Bill & Melinda Gates Foundation.

Erratum to: Health and Demographic Surveillance Systems Within the Child Health and Mortality Prevention Surveillance Network.

Erratum to: Health and Demographic Surveillance Systems Within the Child Health and Mortality Prevention Surveillance Network.

Pathology and Telepathology Methods in the Child Health and Mortality Prevention Surveillance Network.

This manuscript describes the Child Health and Mortality Prevention Surveillance (CHAMPS) network approach to pathologic evaluation of minimally invasive tissue sampling (MITS) specimens, including guidelines for histopathologic examination and further diagnostics with special stains, immunohistochemistry, and molecular testing, as performed at the CHAMPS Central Pathology Laboratory (CPL) at the Centers for Disease Control and Prevention, as well as techniques for virtual discussion of these cases (telepathology) with CHAMPS surveillance locations. Based on review of MITS from the early phase of CHAMPS, the CPL has developed standardized histopathology-based algorithms for achieving diagnoses from MITS and telepathology procedures in conjunction with the CHAMPS sites, with the use of whole slide scanners and digital image archives, for maximizing concurrence and knowledge sharing between site and CPL pathologists. These algorithms and procedures, along with lessons learned from initial implementation of these approaches, guide pathologists at the CPL and CHAMPS sites through standardized diagnostics of MITS cases, and allow for productive, real-time case discussions and consultations.

Standardization of Minimally Invasive Tissue Sampling Specimen Collection and Pathology Training for the Child Health and Mortality Prevention Surveillance Network.

BackgroundMinimally invasive tissue sampling (MITS) is a simplified postmortem examination technique that has shown to be an adequate approach for cause of death investigation in low-resource settings. It requires relatively low level of infrastructures and can be performed by health professionals with no background in pathology. A training program has been developed for the Child Health and Mortality Prevention Surveillance (CHAMPS) network to guarantee standardization of specimen collection techniques, procedures, and laboratory methods.MethodsThe training program has included assessment of the site capacities and training on a standardized protocol of MITS sampling and histological processing. The project has also introduced a program of training for trainers for the personnel from Mozambique. To guarantee the adequacy of the procedure in each site, a trainer accompanied the local teams when the activities started. Training outcomes were assessed by evaluating the quality of the samples obtained and the quality of the slides produced locally.ResultsBetween June 2016 and October 2018, the laboratories of 7 sites (Bangladesh, Ethiopia, Kenya, Mali, Mozambique, Sierra Leone, and South Africa) have been evaluated and upgraded. Training has been delivered to 63 staff members from all sites. More than 600 MITS procedures have been performed. The quantity of tissue obtained in the MITS by the local teams was sufficient or abundant in 73%, and 87% of the slides were considered as technically acceptable or excellent.ConclusionsSatisfactory standardization of MITS and histology procedures has been achieved across all CHAMPS sites through organized capacity-building plans.

Mortality Surveillance Methods to Identify and Characterize Deaths in Child Health and Mortality Prevention Surveillance Network Sites.

Despite reductions over the past 2 decades, childhood mortality remains high in low- and middle-income countries in sub-Saharan Africa and South Asia. In these settings, children often die at home, without contact with the health system, and are neither accounted for, nor attributed with a cause of death. In addition, when cause of death determinations occur, they often use nonspecific methods. Consequently, findings from models currently utilized to build national and global estimates of causes of death are associated with substantial uncertainty. Higher-quality data would enable stakeholders to effectively target interventions for the leading causes of childhood mortality, a critical component to achieving the Sustainable Development Goals by eliminating preventable perinatal and childhood deaths. The Child Health and Mortality Prevention Surveillance (CHAMPS) Network tracks the causes of under-5 mortality and stillbirths at sites in sub-Saharan Africa and South Asia through comprehensive mortality surveillance, utilizing minimally invasive tissue sampling (MITS), postmortem laboratory and pathology testing, verbal autopsy, and clinical and demographic data. CHAMPS sites have established facility- and community-based mortality notification systems, which aim to report potentially eligible deaths, defined as under-5 deaths and stillbirths within a defined catchment area, within 24–36 hours so that MITS can be conducted quickly after death. Where MITS has been conducted, a final cause of death is determined by an expert review panel. Data on cause of death will be provided to local, national, and global stakeholders to inform strategies to reduce perinatal and childhood mortality in sub-Saharan Africa and South Asia.

Health and Demographic Surveillance Systems Within the Child Health and Mortality Prevention Surveillance Network.

Health and demographic surveillance systems (HDSSs) provide a foundation for characterizing and defining priorities and strategies for improving population health. The Child Health and Mortality Prevention Surveillance (CHAMPS) project aims to inform policy to prevent child deaths through generating causes of death from surveillance data combined with innovative diagnostic and laboratory methods. Six of the 7 sites that constitute the CHAMPS network have active HDSSs: Mozambique, Mali, Ethiopia, Kenya, Bangladesh, and South Africa; the seventh, in Sierra Leone, is in the early planning stages. This article describes the network of CHAMPS HDSSs and their role in the CHAMPS project. To generate actionable health and demographic data to prevent child deaths, the network depends on reliable demographic surveillance, and the HDSSs play this crucial role.

Precisely Tracking Childhood Death.

.Little is known about the specific causes of neonatal and under-five childhood death in high-mortality geographic regions due to a lack of primary data and dependence on inaccurate tools, such as verbal autopsy. To meet the ambitious new Sustainable Development Goal 3.2 to eliminate preventable child mortality in every country, better approaches are needed to precisely determine specific causes of death so that prevention and treatment interventions can be strengthened and focused. Minimally invasive tissue sampling (MITS) is a technique that uses needle-based postmortem sampling, followed by advanced histopathology and microbiology to definitely determine cause of death. The Bill & Melinda Gates Foundation is supporting a new surveillance system called the Child Health and Mortality Prevention Surveillance network, which will determine cause of death using MITS in combination with other information, and yield cause-specific population-based mortality rates, eventually in up to 12–15 sites in sub-Saharan Africa and south Asia. However, the Gates Foundation funding alone is not enough. We call on governments, other funders, and international stakeholders to expand the use of pathology-based cause of death determination to provide the information needed to end preventable childhood mortality.