Tracking public health, utilization and outcomes during a pandemic using monitoring surveys
Bibliographic record
Abstract
Surveys can be a critical tool in monitoring public health during emergencies. Existing surveillance systems may provide timely reporting of cases and deaths associated with diseases. However, during COVID-19 they did not provide accurate information on the number of cases with virus-related symptoms, testing and treatment seeking. In fact, the surge of potentially infected individuals seeking diagnosis, testing and treatment represented a serious but largely unmeasured dimension of the crisis. This study aimed to evaluate the potential value of monitoring health, attitudinal, and behavioral dimensions that are not included in current U.S. disease surveillance systems during population health emergencies. Additionally, it seeks to demonstrate the feasibility of designing and implementing a low-cost, rapid-turnaround health and behavioral monitoring system when comparable data from existing surveillance systems are unavailable. From March through November 2020, we conducted national surveys with approximately 1,000 interviews each month with Census-balanced samples from a large national commercial panel. These surveys employed replicate national samples drawn from all 50 US states and the District of Columbia. A total of 9,200 interviews, averaging about 20 min in length, were completed over the course of the nine months of fielding. Nearly a quarter of respondents (22%) reported they had been sick for three days or longer since January with what might be COVID. Respondents were questioned about their symptoms, whether they had seen a doctor, had a confirmatory test for the COVID virus, and test results. Approximately one in ten respondents were currently experiencing COVID-like symptoms each month (95% CI: 10.7-12.0%). These numbers dwarf the 0.3% in April and 3.6% in November who had ever had a COVID positive test result. Moreover, 42% of these symptomatic adults sought medical care or testing, increasing strains on the health care system,. Although surveys may not be needed to estimate diagnosed cases, hospitalizations, or deaths, they can provide the missing data on symptomatic cases in the population, the proportion seeking medical care, ability to obtain a confirmatory test, and reasons for not seeking care or testing. This study demonstrates the ability of surveys to provide such information in a timely fashion, which could be replicated in other countries.
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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 | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".