Environmental exposure to metals and the development of tauopathies, synucleinopathies, and TDP-43 proteinopathies: A systematic evidence map protocol

BACKGROUND

Neurodegenerative diseases such as Alzheimer's disease, Parkinson's disease, and amyotrophic lateral sclerosis are incurable and expected to increase in prevalence in the upcoming decades. Environmental exposure to metals has been suggested as a contributing factor to the development of neurodegenerative disease. This systematic evidence map will identify and characterize the epidemiological and experimental data available on the intersection of eighteen metals of environmental concern (i.e., aluminum, antimony, arsenic, barium, beryllium, cadmium, chromium, cobalt, copper, lead, manganese, mercury, nickel, palladium, radium, silver, vanadium, and zinc) and three neurodegenerative disease clusters (i.e., tauopathies, synucleinopathies, and TDP-43 proteinopathies). We aim to describe the type and amount of evidence available (or lack thereof) for each metal and neurodegenerative disease combination and highlight important knowledge gaps and knowledge clusters for future research.

METHODS

We will conduct a thorough search using two databases (MEDLINE and Web of Science Core Collection) and grey literature resources. Pre-defined criteria have been developed to identify studies which evaluate at least one of the selected metals and neurodegenerative disease-relevant outcomes (e.g., neuropathology, cognitive function, motor function, disease mortality). At each phase of review, studies will be evaluated by two reviewers. Studies determined to be relevant will be extracted for population, exposure, and outcome information. We will conduct a narrative review of the included studies, and the extracted data will be available in a database hosted on Tableau Public.

CONCLUSION

This protocol documents the decisions made a priori to data collection regarding these objectives.

Syndromic surveillance using monthly aggregate health systems information data: methods with application to COVID-19 in Liberia

BACKGROUND

Early detection of SARS-CoV-2 circulation is imperative to inform local public health response. However, it has been hindered by limited access to SARS-CoV-2 diagnostic tests and testing infrastructure. In regions with limited testing capacity, routinely collected health data might be leveraged to identify geographical locales experiencing higher than expected rates of COVID-19-associated symptoms for more specific testing activities.

METHODS

We developed syndromic surveillance tools to analyse aggregated health facility data on COVID-19-related indicators in seven low- and middle-income countries (LMICs), including Liberia. We used time series models to estimate the expected monthly counts and 95% prediction intervals based on 4 years of previous data. Here, we detail and provide resources for our data preparation procedures, modelling approach and data visualisation tools with application to Liberia.

RESULTS

To demonstrate the utility of these methods, we present syndromic surveillance results for acute respiratory infections (ARI) at health facilities in Liberia during the initial months of the COVID-19 pandemic (January through August 2020). For each month, we estimated the deviation between the expected and observed number of ARI cases for 325 health facilities and 15 counties to identify potential areas of SARS-CoV-2 circulation.

CONCLUSIONS

Syndromic surveillance can be used to monitor health facility catchment areas for spikes in specific symptoms which may indicate SARS-CoV-2 circulation. The developed methods coupled with the existing infrastructure for routine health data systems can be leveraged to monitor a variety of indicators and other infectious diseases with epidemic potential.