Dataset Information

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

ABSTRACT: 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.

SUBMITTER: Salzberg NT

PROVIDER: S-EPMC6785672 | biostudies-literature | 2019 Oct

REPOSITORIES: biostudies-literature

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Publications

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

Salzberg Navit T NT Sivalogan Kasthuri K Bassat Quique Q Taylor Allan W AW Adedini Sunday S El Arifeen Shams S Assefa Nega N Blau Dianna M DM Chawana Richard R Cain Carrie Jo CJ Cain Kevin P KP Caneer J Patrick JP Garel Mischka M Gurley Emily S ES Kaiser Reinhard R Kotloff Karen L KL Mandomando Inacio I Morris Timothy T Nyamthimba Onyango Peter P Sazzad Hossain M S HMS Scott J Anthony G JAG Seale Anna C AC Sitoe Antonio A Sow Samba O SO Tapia Milagritos D MD Whitney Ellen A EA Worrell Mary Claire MC Zielinski-Gutierrez Emily E Madhi Shabir A SA Raghunathan Pratima L PL Koplan Jeffrey P JP Breiman Robert F RF

Clinical infectious diseases : an official publication of the Infectious Diseases Society of America 20191001 Suppl 4

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 ...[more]

PMID: 31598664

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Project description:BackgroundLower respiratory tract infections are a leading cause of death in young children, but few studies have collected the specimens needed to define the role of specific causes. The Child Health and Mortality Prevention Surveillance (CHAMPS) platform aims to investigate causes of death in children aged <5 years in high-mortality rate settings, using postmortem minimally invasive tissue sampling and other advanced diagnostic techniques. We examined findings for deaths identified in CHAMPS sites in 7 countries in sub-Saharan Africa and south Asia to evaluate the role of respiratory syncytial virus (RSV).MethodsWe included deaths that occurred between December 2016 and December 2019. Panels determined causes of deaths by reviewing all available data including pathological results from minimally invasive tissue sampling, polymerase chain reaction screening for multiple infectious pathogens in lung tissue, nasopharyngeal swab, blood, and cerebrospinal fluid samples, clinical information from medical records, and verbal autopsies.ResultsWe evaluated 1213 deaths, including 695 in neonates (aged <28 days), 283 in infants (28 days to <12 months), and 235 in children (12-59 months). RSV was detected in postmortem specimens in 67 of 1213 deaths (5.5%); in 24 deaths (2.0% of total), RSV was determined to be a cause of death, and it contributed to 5 other deaths. Younger infants (28 days to <6 months of age) accounted for half of all deaths attributed to RSV; 6.5% of all deaths in younger infants were attributed to RSV. RSV was the underlying and only cause in 4 deaths; the remainder (n = 20) had a median of 2 (range, 1-5) other conditions in the causal chain. Birth defects (n = 8) and infections with other pathogens (n = 17) were common comorbid conditions.ConclusionsRSV is an important cause of child deaths, particularly in young infants. These findings add to the substantial body of literature calling for better treatment and prevention options for RSV in high-mortality rate settings.

Project description:BackgroundInvasive Group B Streptococcus (GBS) is a common cause of early-onset neonatal sepsis and is also associated with stillbirth. This study aimed to determine the proportion of stillborn infants and infants who died between 0 and 90 days attributable to GBS using postmortem minimally invasive tissue sampling (MITS) in 7 low- and middle-income countries (LMICs) participating in Child Health and Mortality Prevention Surveillance (CHAMPS).MethodsDeaths that occurred between December 2016 and December 2021 were investigated with MITS, including culture for bacteria of blood and cerebrospinal fluid (CSF), multipathogen polymerase chain reaction on blood, CSF, and lung tissue and histopathology of lung, liver, and brain. Data collection included clinical record review and verbal autopsy. Expert panels reviewed all information and assigned causes of death.ResultsWe evaluated 2966 deaths, including stillborn infants (n = 1322), infants who died during first day of life (0 to <24 hours, n = 597), early neonatal deaths (END) (1 day to <7 days; END; n = 593), and deaths from 7 to 90 days (n = 454). Group B Streptococcus was determined to be in the causal pathway of death for 2.7% of infants (79 of 2, 966; range, 0.3% in Sierra Leone to 7.2% in South Africa), including 2.3% (31 of 1322) of stillbirths, 4.7% (28 of 597) 0 to <24 hours, 1.9% (11 of 593) END, and 2.0% (9 of 454) of deaths from 7 to 90 days of age. Among deaths attributed to GBS with birth weight data available, 61.9% (39 of 63) of decedents weighed <2500 grams at birth. Group B Streptococcus sepsis was the postmortem diagnosis for 100% (31 of 31) of stillbirths. For deaths <90 days, postmortem diagnoses included GBS sepsis (83.3%, 40 of 48), GBS meningitis (4.2%, 2 of 48), and GBS pneumonia (2.1%, 1 of 48).ConclusionsOur study reveals significant heterogeneity in the contribution of invasive GBS disease to infant mortality across different countries, emphasizing the need for tailored prevention strategies. Moreover, our findings highlight the substantial impact of GBS on stillbirths, shedding light on a previously underestimated aspect in LMICs.

Project description:Each year, 2.4 million children die within their first month of life. Child Health and Mortality Prevention Surveillance (CHAMPS) established in 7 countries aims to generate accurate data on why such deaths occur and inform prevention strategies. Neonatal deaths that occurred between December 2016 and December 2021 were investigated with MITS within 24-72 hours of death. Testing included blood, cerebrospinal fluid and lung cultures, multi-pathogen PCR on blood, CSF, nasopharyngeal swabs and lung tissue, and histopathology examination of lung, liver and brain. Data collection included clinical record review and family interview using standardized verbal autopsy. The full set of data was reviewed by local experts using a standardized process (Determination of Cause of Death) to identify all relevant conditions leading to death (causal chain), per WHO recommendations. For analysis we stratified neonatal death into 24-hours of birth, early (1-<7 days) and late (7-<28 days) neonatal deaths. We analyzed 1458 deaths, 41% occurring within 24-hours, 41% early and 18% late neonatal deaths. Leading underlying causes of death were complications of intrapartum events (31%), complications of prematurity (28%), infections (17%), respiratory disorders (11%), and congenital malformations (8%). In addition to the underlying cause, 62% of deaths had additional conditions and 14% had ≥3 other conditions in the causal chain. The most common causes considering the whole causal chain were infection (40%), prematurity (32%) and respiratory distress syndrome (28%). Common maternal conditions linked to neonatal death were maternal hypertension (10%), labour and delivery complications (8%), multiple gestation (7%), placental complications (6%) obstructed labour and chorioamnionitis (5%, each). CHAMPS' findings showing the full causal chain of events that lead to death, in addition to maternal factors, highlights the complexities involved in each death along with the multiple opportunities for prevention. Highlighting improvements to prenatal and obstetric care and infection prevention are urgently needed in high-mortality settings.

Project description:BackgroundFunding from external agencies for malaria control in Africa has increased dramatically over the past decade resulting in substantial increases in population coverage by effective malaria prevention interventions. This unprecedented effort to scale-up malaria interventions is likely improving child survival and will likely contribute to meeting Millennium Development Goal (MDG) 4 to reduce the < 5 mortality rate by two thirds between 1990 and 2015.MethodsThe Lives Saved Tool (LiST) model was used to quantify the likely impact that malaria prevention intervention scale-up has had on malaria mortality over the past decade (2001-2010) across 43 malaria endemic countries in sub-Saharan African. The likely impact of ITNs and malaria prevention interventions in pregnancy (intermittent preventive treatment [IPTp] and ITNs used during pregnancy) over this period was assessed.ResultsThe LiST model conservatively estimates that malaria prevention intervention scale-up over the past decade has prevented 842,800 (uncertainty: 562,800-1,364,645) child deaths due to malaria across 43 malaria-endemic countries in Africa, compared to a baseline of the year 2000. Over the entire decade, this represents an 8.2% decrease in the number of malaria-caused child deaths that would have occurred over this period had malaria prevention coverage remained unchanged since 2000. The biggest impact occurred in 2010 with a 24.4% decrease in malaria-caused child deaths compared to what would have happened had malaria prevention interventions not been scaled-up beyond 2000 coverage levels. ITNs accounted for 99% of the lives saved.ConclusionsThe results suggest that funding for malaria prevention in Africa over the past decade has had a substantial impact on decreasing child deaths due to malaria. Rapidly achieving and then maintaining universal coverage of these interventions should be an urgent priority for malaria control programmes in the future. Successful scale-up in many African countries will likely contribute substantially to meeting MDG 4, as well as succeed in meeting MDG 6 (Target 1) to halt and reverse malaria incidence by 2015.

Project description:BackgroundStudying COVID-19 misinformation on Twitter presents methodological challenges. A computational approach can analyze large data sets, but it is limited when interpreting context. A qualitative approach allows for a deeper analysis of content, but it is labor-intensive and feasible only for smaller data sets.ObjectiveWe aimed to identify and characterize tweets containing COVID-19 misinformation.MethodsTweets geolocated to the Philippines (January 1 to March 21, 2020) containing the words coronavirus, covid, and ncov were mined using the GetOldTweets3 Python library. This primary corpus (N=12,631) was subjected to biterm topic modeling. Key informant interviews were conducted to elicit examples of COVID-19 misinformation and determine keywords. Using NVivo (QSR International) and a combination of word frequency and text search using key informant interview keywords, subcorpus A (n=5881) was constituted and manually coded to identify misinformation. Constant comparative, iterative, and consensual analyses were used to further characterize these tweets. Tweets containing key informant interview keywords were extracted from the primary corpus and processed to constitute subcorpus B (n=4634), of which 506 tweets were manually labeled as misinformation. This training set was subjected to natural language processing to identify tweets with misinformation in the primary corpus. These tweets were further manually coded to confirm labeling.ResultsBiterm topic modeling of the primary corpus revealed the following topics: uncertainty, lawmaker's response, safety measures, testing, loved ones, health standards, panic buying, tragedies other than COVID-19, economy, COVID-19 statistics, precautions, health measures, international issues, adherence to guidelines, and frontliners. These were categorized into 4 major topics: nature of COVID-19, contexts and consequences, people and agents of COVID-19, and COVID-19 prevention and management. Manual coding of subcorpus A identified 398 tweets with misinformation in the following formats: misleading content (n=179), satire and/or parody (n=77), false connection (n=53), conspiracy (n=47), and false context (n=42). The discursive strategies identified were humor (n=109), fear mongering (n=67), anger and disgust (n=59), political commentary (n=59), performing credibility (n=45), overpositivity (n=32), and marketing (n=27). Natural language processing identified 165 tweets with misinformation. However, a manual review showed that 69.7% (115/165) of tweets did not contain misinformation.ConclusionsAn interdisciplinary approach was used to identify tweets with COVID-19 misinformation. Natural language processing mislabeled tweets, likely due to tweets written in Filipino or a combination of the Filipino and English languages. Identifying the formats and discursive strategies of tweets with misinformation required iterative, manual, and emergent coding by human coders with experiential and cultural knowledge of Twitter. An interdisciplinary team composed of experts in health, health informatics, social science, and computer science combined computational and qualitative methods to gain a better understanding of COVID-19 misinformation on Twitter.

Dataset Information

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

Publications

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

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