Evaluation of the Global Health Security Index as a predictor of COVID-19 excess mortality standardised for under-reporting and age structure.

BACKGROUND: Previous studies have observed that countries with the strongest levels of pandemic preparedness capacities experience the greatest levels of COVID-19 burden. However, these analyses have been limited by cross-country differentials in surveillance system quality and demographics. Here, we address limitations of previous comparisons by exploring country-level relationships between pandemic preparedness measures and comparative mortality ratios (CMRs), a form of indirect age standardisation, of excess COVID-19 mortality. METHODS: We indirectly age standardised excess COVID-19 mortality, from the Institute for Health Metrics and Evaluation modelling database, by comparing observed total excess mortality to an expected age-specific COVID-19 mortality rate from a reference country to derive CMRs. We then linked CMRs with data on country-level measures of pandemic preparedness from the Global Health Security (GHS) Index. These data were used as input into multivariable linear regression analyses that included income as a covariate and adjusted for multiple comparisons. We conducted a sensitivity analysis using excess mortality estimates from WHO and The Economist. RESULTS: The GHS Index was negatively associated with excess COVID-19 CMRs (table 2; ß= -0.21, 95% CI= -0.35 to -0.08). Greater capacities related to prevention (ß= -0.11, 95% CI= -0.22 to -0.00), detection (ß= -0.09, 95% CI= -0.19 to -0.00), response (ß = -0.19, 95% CI= -0.36 to -0.01), international commitments (ß= -0.17, 95% CI= -0.33 to -0.01) and risk environments (ß= -0.30, 95% CI= -0.46 to -0.15) were each associated with lower CMRs. Results were not replicated using excess mortality models that rely more heavily on reported COVID-19 deaths (eg, WHO and The Economist). CONCLUSION: The first direct comparison of COVID-19 excess mortality rates across countries accounting for under-reporting and age structure confirms that greater levels of preparedness were associated with lower excess COVID-19 mortality. Additional research is needed to confirm these relationships as more robust national-level data on COVID-19 impact become available.

Genomic analysis, immunomodulation and deep phenotyping of patients with nodding syndrome.

The aetiology of nodding syndrome remains unclear, and comprehensive genotyping and phenotyping data from patients remain sparse. Our objectives were to characterize the phenotype of patients with nodding syndrome, investigate potential contributors to disease aetiology, and evaluate response to immunotherapy. This cohort study investigated members of a single-family unit from Lamwo District, Uganda. The participants for this study were selected by the Ugandan Ministry of Health as representative for nodding syndrome and with a conducive family structure for genomic analyses. Of the eight family members who participated in the study at the National Institutes of Health (NIH) Clinical Center, three had nodding syndrome. The three affected patients were extensively evaluated with metagenomic sequencing for infectious pathogens, exome sequencing, spinal fluid immune analyses, neurometabolic and toxicology testing, continuous electroencephalography and neuroimaging. Five unaffected family members underwent a subset of testing for comparison. A distinctive interictal pattern of sleep-activated bursts of generalized and multifocal epileptiform discharges and slowing was observed in two patients. Brain imaging showed two patients had mild generalized cerebral atrophy, and both patients and unaffected family members had excessive metal deposition in the basal ganglia. Trace metal biochemical evaluation was normal. CSF was non-inflammatory and one patient had CSF-restricted oligoclonal bands. Onchocerca volvulus-specific antibodies were present in all patients and skin snips were negative for active onchocerciasis. Metagenomic sequencing of serum and CSF revealed hepatitis B virus in the serum of one patient. Vitamin B6 metabolites were borderline low in all family members and CSF pyridoxine metabolites were normal. Mitochondrial DNA testing was normal. Exome sequencing did not identify potentially causal candidate gene variants. Nodding syndrome is characterized by a distinctive pattern of sleep-activated epileptiform activity. The associated growth stunting may be due to hypothalamic dysfunction. Extensive testing years after disease onset did not clarify a causal aetiology. A trial of immunomodulation (plasmapheresis in two patients and intravenous immunoglobulin in one patient) was given without short-term effect, but longer-term follow-up was not possible to fully assess any benefit of this intervention.

Toward Understanding Death.

Evidence-based approaches to preventing child death require evidence; without data on common causes of child mortality, taking effective action to prevent these deaths is difficult at best. Minimally invasive tissue sampling (MITS) is a potentially powerful, but nascent, technique to obtain gold standard information on causes of death. The Gates Foundation committed to further establishing the methodology and obtain the highest quality information on the major causes of death for children under 5 years. In 2018, the MITS Surveillance Alliance was launched to implement, refine, and enhance the use of MITS across high mortality settings. The Alliance and its members have contributed to some remarkable opportunities to improve mortality surveillance, and we have only just begun to understand the possibilities on larger scales. This supplement showcases studies conducted by MITS Surveillance Alliance members and represents a significant contribution to the cause-of-death literature from high mortality settings.

Younger ages at risk of Covid-19 mortality in communities of color.

More than 85% of Covid-19 mortality in high income countries is among people 65 years of age or older. Recent disaggregated data from the UK and US show that minority communities have increased mortality among younger age groups and in South Africa initial data suggest that the majority of deaths from Covid-19 are under 65 years of age. These observations suggest significant potential for increased Covid-19 mortality among younger populations in Africa and South Asia and may impact age-based selection of high-risk groups eligible for a future vaccine.

Initial findings from a novel population-based child mortality surveillance approach: a descriptive study.

BACKGROUND: Sub-Saharan Africa and south Asia contributed 81% of 5·9 million under-5 deaths and 77% of 2·6 million stillbirths worldwide in 2015. Vital registration and verbal autopsy data are mainstays for the estimation of leading causes of death, but both are non-specific and focus on a single underlying cause. We aimed to provide granular data on the contributory causes of death in stillborn fetuses and in deceased neonates and children younger than 5 years, to inform child mortality prevention efforts. METHODS: The Child Health and Mortality Prevention Surveillance (CHAMPS) Network was established at sites in seven countries (Baliakandi, Bangladesh; Harar and Kersa, Ethiopia; Siaya and Kisumu, Kenya; Bamako, Mali; Manhiça, Mozambique; Bombali, Sierra Leone; and Soweto, South Africa) to collect standardised, population-based, longitudinal data on under-5 mortality and stillbirths in sub-Saharan Africa and south Asia, to improve the accuracy of determining causes of death. Here, we analysed data obtained in the first 2 years after the implementation of CHAMPS at the first five operational sites, during which surveillance and post-mortem diagnostics, including minimally invasive tissue sampling (MITS), were used. Data were abstracted from all available clinical records of deceased children, and relevant maternal health records were also extracted for stillbirths and neonatal deaths, to incorporate reported pregnancy or delivery complications. Expert panels followed standardised procedures to characterise causal chains leading to death, including underlying, intermediate (comorbid or antecedent causes), and immediate causes of death for stillbirths, neonatal deaths, and child (age 1-59 months) deaths. FINDINGS: Between Dec 10, 2016, and Dec 31, 2018, MITS procedures were implemented at five sites in Mozambique, South Africa, Kenya, Mali, and Bangladesh. We screened 2385 death notifications for inclusion eligibility, following which 1295 families were approached for consent; consent was provided for MITS by 963 (74%) of 1295 eligible cases approached. At least one cause of death was identified in 912 (98%) of 933 cases (180 stillbirths, 449 neonatal deaths, and 304 child deaths); two or more conditions were identified in the causal chain for 585 (63%) of 933 cases. The most common underlying causes of stillbirth were perinatal asphyxia or hypoxia (130 [72%] of 180 stillbirths) and congenital infection or sepsis (27 [15%]). The most common underlying causes of neonatal death were preterm birth complications (187 [42%] of 449 neonatal deaths), perinatal asphyxia or hypoxia (98 [22%]), and neonatal sepsis (50 [11%]). The most common underlying causes of child deaths were congenital birth defects (39 [13%] of 304 deaths), lower respiratory infection (37 [12%]), and HIV (35 [12%]). In 503 (54%) of 933 cases, at least one contributory pathogen was identified. Cytomegalovirus, Escherichia coli, group B Streptococcus, and other infections contributed to 30 (17%) of 180 stillbirths. Among neonatal deaths with underlying prematurity, 60% were precipitated by other infectious causes. Of the 275 child deaths with infectious causes, the most common contributory pathogens were Klebsiella pneumoniae (86 [31%]), Streptococcus pneumoniae (54 [20%]), HIV (40 [15%]), and cytomegalovirus (34 [12%]), and multiple infections were common. Lower respiratory tract infection contributed to 174 (57%) of 304 child deaths. INTERPRETATION: Cause of death determination using MITS enabled detailed characterisation of contributing conditions. Global estimates of child mortality aetiologies, which are currently based on a single syndromic cause for each death, will be strengthened by findings from CHAMPS. This approach adds specificity and provides a more complete overview of the chain of events leading to death, highlighting multiple potential interventions to prevent under-5 mortality and stillbirths. FUNDING: Bill & Melinda Gates Foundation.

Why Child Health and Mortality Prevention Surveillance?

Recognizing the need for better primary data on the causes of global child mortality, the Bill & Melinda Gates Foundation made an unusually long funding commitment toward a surveillance system using pathology to identify opportunities to prevent child deaths and promote equity.

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.

Nodding syndrome may be an autoimmune reaction to the parasitic worm Onchocerca volvulus.

Nodding syndrome is an epileptic disorder of unknown etiology that occurs in children in East Africa. There is an epidemiological association with Onchocerca volvulus, the parasitic worm that causes onchocerciasis (river blindness), but there is limited evidence that the parasite itself is neuroinvasive. We hypothesized that nodding syndrome may be an autoimmune-mediated disease. Using protein chip methodology, we detected autoantibodies to leiomodin-1 more abundantly in patients with nodding syndrome compared to unaffected controls from the same village. Leiomodin-1 autoantibodies were found in both the sera and cerebrospinal fluid of patients with nodding syndrome. Leiomodin-1 was found to be expressed in mature and developing human neurons in vitro and was localized in mouse brain to the CA3 region of the hippocampus, Purkinje cells in the cerebellum, and cortical neurons, structures that also appear to be affected in patients with nodding syndrome. Antibodies targeting leiomodin-1 were neurotoxic in vitro, and leiomodin-1 antibodies purified from patients with nodding syndrome were cross-reactive with O. volvulus antigens. This study provides initial evidence supporting the hypothesis that nodding syndrome is an autoimmune epileptic disorder caused by molecular mimicry with O. volvulus antigens and suggests that patients may benefit from immunomodulatory therapies.

Sennetsu Neorickettsiosis, Spotted Fever Group, and Typhus Group Rickettsioses in Three Provinces in Thailand.

We estimated the seroprevalence and determined the frequency of acute infections with Neorickettsia sennetsu, spotted fever group rickettsiae, Rickettsia typhi, and Orientia tsutsugamushi among 2,225 febrile patients presenting to community hospitals in three rural Thailand provinces during 2002-2005. The seroprevalence was 0.2% for sennetsu neorickettsiosis (SN), 0.8% for spotted fever group (SFG) rickettsiae, 4.2% for murine typhus (MT), and 4.2% for scrub typhus (ST). The frequency of acute infections was 0.1% for SN, 0.6% for SFG, 2.2% for MT, and 1.5% for ST. Additional studies to confirm the distribution of these pathogens and to identify animal reservoirs and transmission cycles are needed to understand the risk of infection.

Prioritising Infectious Disease Mapping.

BACKGROUND: Increasing volumes of data and computational capacity afford unprecedented opportunities to scale up infectious disease (ID) mapping for public health uses. Whilst a large number of IDs show global spatial variation, comprehensive knowledge of these geographic patterns is poor. Here we use an objective method to prioritise mapping efforts to begin to address the large deficit in global disease maps currently available. METHODOLOGY/PRINCIPAL FINDINGS: Automation of ID mapping requires bespoke methodological adjustments tailored to the epidemiological characteristics of different types of diseases. Diseases were therefore grouped into 33 clusters based upon taxonomic divisions and shared epidemiological characteristics. Disability-adjusted life years, derived from the Global Burden of Disease 2013 study, were used as a globally consistent metric of disease burden. A review of global health stakeholders, existing literature and national health priorities was undertaken to assess relative interest in the diseases. The clusters were ranked by combining both metrics, which identified 44 diseases of main concern within 15 principle clusters. Whilst malaria, HIV and tuberculosis were the highest priority due to their considerable burden, the high priority clusters were dominated by neglected tropical diseases and vector-borne parasites. CONCLUSIONS/SIGNIFICANCE: A quantitative, easily-updated and flexible framework for prioritising diseases is presented here. The study identifies a possible future strategy for those diseases where significant knowledge gaps remain, as well as recognising those where global mapping programs have already made significant progress. For many conditions, potential shared epidemiological information has yet to be exploited.

Infectious causes of encephalitis and meningoencephalitis in Thailand, 2003-2005.

Acute encephalitis is a severe neurologic syndrome. Determining etiology from among ≈100 possible agents is difficult. To identify infectious etiologies of encephalitis in Thailand, we conducted surveillance in 7 hospitals during July 2003-August 2005 and selected patients with acute onset of brain dysfunction with fever or hypothermia and with abnormalities seen on neuroimages or electroencephalograms or with cerebrospinal fluid pleocytosis. Blood and cerebrospinal fluid were tested for >30 pathogens. Among 149 case-patients, median age was 12 (range 0-83) years, 84 (56%) were male, and 15 (10%) died. Etiology was confirmed or probable for 54 (36%) and possible or unknown for 95 (64%). Among confirmed or probable etiologies, the leading pathogens were Japanese encephalitis virus, enteroviruses, and Orientia tsutsugamushi. No samples were positive for chikungunya, Nipah, or West Nile viruses; Bartonella henselae; or malaria parasites. Although a broad range of infectious agents was identified, the etiology of most cases remains unknown.

Rapidly building global health security capacity--Uganda demonstration project, 2013.

Increasingly, the need to strengthen global capacity to prevent, detect, and respond to public health threats around the globe is being recognized. CDC, in partnership with the World Health Organization (WHO), has committed to building capacity by assisting member states with strengthening their national capacity for integrated disease surveillance and response as required by International Health Regulations (IHR). CDC and other U.S. agencies have reinforced their pledge through creation of global health security (GHS) demonstration projects. One such project was conducted during March-September 2013, when the Uganda Ministry of Health (MoH) and CDC implemented upgrades in three areas: 1) strengthening the public health laboratory system by increasing the capacity of diagnostic and specimen referral networks, 2) enhancing the existing communications and information systems for outbreak response, and 3) developing a public health emergency operations center (EOC) (Figure 1). The GHS demonstration project outcomes included development of an outbreak response module that allowed reporting of suspected cases of illness caused by priority pathogens via short messaging service (SMS; i.e., text messaging) to the Uganda District Health Information System (DHIS-2) and expansion of the biologic specimen transport and laboratory reporting system supported by the President's Emergency Plan for AIDS Relief (PEPFAR). Other enhancements included strengthening laboratory management, establishing and equipping the EOC, and evaluating these enhancements during an outbreak exercise. In 6 months, the project demonstrated that targeted enhancements resulted in substantial improvements to the ability of Uganda's public health system to detect and respond to health threats.