COVID-19 Health Impact: A Use Case for Syndromic Surveillance System Monitoring Based on Primary Care Patient Registries in the Netherlands
Bibliographic record
Abstract
Background: The COVID-19 pandemic challenged societies worldwide. The implementation of mitigation measures to limit the number of SARS-CoV-2 infections resulted in unintended health effects. Objective: The objective of this study is to demonstrate the use of an existing syndromic surveillance system in primary care during a first series of quarterly cross-sectional monitoring cycles, targeting health problems presented in primary care among Dutch youth since August 2021. Methods: Aggregated data from the surveillance system of Nivel Primary Care Database were analyzed quarterly to monitor 20 health problems often reported in the aftermath of disasters and environmental incidents. Results were stratified by age (ie, 0-4, 5-14, and 15-24 years), sex, and region (province). Weekly prevalence rates were calculated as the number of persons consulting their general practitioner in a particular week, using the number of enlisted persons as the denominator. Findings were compared to quarterly survey panel data, collected in the context of the Integrated Health Monitor COVID-19, and the Dutch stringency index values, indicative of the intensity of COVID-19 mitigation measures. Results: Over time, weekly rates pointed to an increased number of consultations for depressive feelings and suicide (attempts) among youth, during and after periods with intensified domestic restrictions. Conclusions: The results illustrate how, from a disaster health research perspective based on the COVID-19 pandemic, health consequences of pandemics could be successfully followed over time using an existing infrastructure for syndromic surveillance and monitoring. Particular areas of health concern can be defined beforehand, and may be modified or expanded during the monitoring activities to track relevant developments. Although an association between patterns and changes in the strictness of mitigation measures might seem probable, claims about causality should be made with caution.
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 imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.034 | 0.089 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".