MétaCan
Menu
Back to cohort
Record W7037530605

The Epidemiology of Acute Gastrointestinal Illness in Ethiopia, Mozambique, Nigeria, and Tanzania

2022· dissertation· en· W7037530605 on OpenAlexaboutno aff

Bibliographic record

VenueUWSpace (University of Waterloo) · 2022
Typedissertation
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicLepidoptera: Biology and Taxonomy
Canadian institutionsnot available
Fundersnot available
KeywordsEpidemiologyTanzaniaIncidence (geometry)PopulationGeneral partnershipRural populationPublic health
DOInot available

Abstract

fetched live from OpenAlex

Gastrointestinal infections transmitted by food are a global concern and most severe in African low-and-middle-income countries (LMICs), though in these countries accurate data on acute gastrointestinal illness (AGI) are lacking. The thesis aimed to estimate the epidemiology of AGI in Ethiopia, Mozambique, Nigeria, and Tanzania; because this research was interrupted by the COVID-19 pandemic, a secondary aim was to explore application of methods typically used to adjust for under-reporting of foodborne infections to COVID-19. The thesis objectives were to: describe the epidemiology of AGI at the population level in Ethiopia, Mozambique, Nigeria, and Tanzania; evaluate the multi-national collaborative process used to achieve the first objective; and apply methods used for foodborne infections to estimate the under-ascertainment multipliers for each step in the reporting chain for COVID-19 for an example setting with available data (Toronto, Canada). To determine the epidemiology of AGI, a population survey was conducted in one urban and one rural site in each of Ethiopia, Mozambique, Nigeria, and Tanzania, from October 01, 2020 to September 30, 2021, using both web-based and face-to-face survey tools (n=4487). The incidence of AGI (0.5 episodes per person-year) was comparable or lower to other LMICs, the duration (4 days) appeared slightly longer or comparable to other LMICs, and although age was a significant risk factor, gender was not. The multi-national collaboration that supported this population survey was evaluated using Larkan et al.’s (2016) framework and its seven core concepts: focus, values, equity, benefit, communication, leadership, and resolution. The evaluation identified that the partnership considered the interplay and balance between operations and relations, and featured a shared goal, mutual benefits, transparency, inclusiveness, and leadership attributes. A slight working culture difference was noted, with a need to enhance responsibility-sharing and dedication. Finally, application of foodborne underreporting adjustment methods to COVID-19 was done for Toronto, Canada, where all necessary data sources (de-identified reported case data, weekly testing data, and population survey data) were available. Specifically, stochastic modelling was applied to estimate the under-ascertainment rate of COVID-19 in Toronto at early stages from March 2020 (the beginning of the pandemic) through May 23, 2020. Overall, 1 in 18 COVID-19 infections that occurred in the community were reported to Toronto Public Health. The under-ascertainment approach yielded comparable estimates to seroprevalence studies, and this approach allowed identification of where cases were lost in the reporting chain. In conclusion, this thesis identified that in Ethiopia, Mozambique, Nigeria, and Tanzania, AGI appears to pose a considerable incidence that suggests regular surveillance and intervention are needed. Future population surveys or other collaborative burden of infectious disease studies in African or other LMICs may benefit from partnerships that consider the interplay and balance between operations and relations and leadership attributes (and their dependent resolution strategies) such as the full and equitable delegation of tasks that eliminated hierarchical positionality and the flexibility to altering some premade decisions and executing action points. Finally, the COVID-19 pandemic provided an opportunity to apply the under-ascertainment measurement approach used for foodborne infections and influenza to COVID-19, and this thesis demonstrated that the under-ascertainment method typically applied to foodborne infections is useful for other infectious diseases under public health surveillance.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.043
Threshold uncertainty score0.086

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.009
GPT teacher head0.227
Teacher spread0.218 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueUWSpace (University of Waterloo)Same topicLepidoptera: Biology and TaxonomyFrench-language works237,207