Evaluation of the New York City COVID-19 case investigation and contact tracing program: a cascade of care analysis
Bibliographic record
Abstract
BACKGROUND: New York City (NYC) was the first COVID-19 epicenter in the United States and home to one of the country's largest contact tracing programs, NYC Test & Trace (T2). Understanding points of attrition along the stages of program implementation and follow-up can inform contact tracing efforts for future epidemics or pandemics. The objective of this study was to evaluate the completeness and timeliness of T2 case and contact notification and monitoring using a "cascade of care" approach. METHODS: This cross-sectional study included all SARS-CoV-2 cases and contacts reported to T2 from May 31, 2020 to January 1, 2022. Attrition along the "cascade of care" was defined as: (1) attempted, (2) reached, (3) completed intake (main outcome), (4) eligible for monitoring, and (5) successfully monitored. Timeliness was assessed: (1) by median days from a case's date of testing until their positive result was reported to T2, (2) from result until the case was notified by T2, and (3) from a case report of a contact until notification of the contact. RESULTS: A total of 1.45 million cases and 1.38 million contacts were reported to T2 during this period. For cases, attrition occurred evenly across the first three cascade steps (~-12%) and did not change substantially until the Omicron wave in December 2021. During the Omicron wave, the proportion of cases attempted dropped precipitously. For contacts, the largest attrition occurred between attempting and reaching (-27%), and attrition rose with each COVID-19 wave as contact volumes increased. Attempts to reach contacts discontinued entirely during the Omicron wave. Overall, 67% of cases and 49% of contacts completed intake interviews (79% and 57% prior to Omicron). T2 was timely, with a median of 1 day to receive lab results, 2 days to notify cases, and < 1 day to notify contacts. CONCLUSIONS: T2 provided a large volume of NYC residents with timely notification and monitoring. Engagement in the program was lower for contacts than cases, with the largest gap coming from inability to reach individuals during call attempts. To strengthen future test-and-trace efforts, strategies are needed to encourage acceptance of local contact tracer outreach attempts.
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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 | 0.045 | 0.098 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".