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Record W4220888310 · doi:10.1016/s2214-109x(22)00133-4

Syndromic surveillance with monthly aggregate health systems information data for COVID-19 pandemic response in Neno, Malawi: a monitoring study

2022· article· en· W4220888310 on OpenAlexaboutno aff
Moses Banda Aron, Emilia Connolly, Fabien Munyaneza, Donald Fejfar, Isaac Mphande, George Talama, Chiyembekezo Kachimanga, Brown David Khongo, Isabel Fulcher

Bibliographic record

VenueThe Lancet Global Health · 2022
Typearticle
Languageen
FieldMathematics
TopicCOVID-19 epidemiological studies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicinePandemicPublic healthContext (archaeology)Public health surveillanceMalariaCoronavirus disease 2019 (COVID-19)OutbreakPsychological interventionEnvironmental healthHealth facilityDemographyEpidemiologyPediatricsPopulationGeographyDiseaseVirologyInternal medicineInfectious disease (medical specialty)Health servicesImmunology

Abstract

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Background In the context of diminutive COVID-19 screening and testing, syndromic surveillance can be used to identify areas with higher-than-expected SARS-CoV-2 symptoms for targeted public health interventions. We used syndromic surveillance to monitor potential SARS-CoV-2 outbreaks in 14 health facilities in the Neno district of rural Malawi. Methods We monitored three indicators identified as potential symptoms of SARS-CoV-2 infection: the proportion of outpatient visits for fast-breathing cases in children under 5 years (FBC<5); the proportion of suspected malaria cases confirmed as non-malaria in children under 5 years (NMC<5); and the same indicator in individuals aged 5 years and older (NMC≥5). We extracted data aggregated by month and at the health facility-level from the District Health Information System. With data from January, 2016, to February, 2020, as a baseline, we used a linear model with a negative binomial distribution to estimate expected proportions for the indicators in absence of the COVID-19 pandemic with 95% prediction intervals (PI) for March, 2020, to July, 2021. We compared the observed proportions to the expected rates, focusing on the first two waves of infections (June to July, 2020, and January to March, 2021). Findings The proportion of FBC<5 was consistently higher than expected, with a peak in May, 2020, when 2·5% of outpatient visits were fast breathing cases in children younger than 5 years of age (compared with the expected rate of 0·8% [95% PI 0·4–1·5]). NMC<5 was as expected throughout the study period. The NMC≥5 indicator remained as expected, except for increases in suspected cases tested negative for malaria, to 31·3% (from the expected 18·6% [95% PI 12·3–28·7]) in November, 2020, and to 32·5% (from the expected 21·7% [95% PI 14·2–32·2]) in July, 2021. Interpretation An increase in FBC<5 and NMC≥5 before observed COVID-19 waves might indicate SARS-CoV-2 infections that were missed before robust testing. This tendency was not seen in NMC<5, which can represent differences in symptomatology leading to decreased health-seeking behaviours for this age group. Syndromic surveillance can allow for real-time responses at facilities, including increased and focused testing and screening to identify potential SARS-CoV-2 infections. Funding Supported by Canadian Institutes of Health Research.

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)0.004
metaresearch head score (Gemma)0.012
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 score0.130
Threshold uncertainty score0.259

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.401
GPT teacher head0.504
Teacher spread0.103 · 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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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