Evaluating completion rates of COVID-19 contact tracing surveys in New York City
Importance Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. Objective To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. Methods We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. Results The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation – compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% – 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% –2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% – 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%. Conclusion These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities..
| Medienart: |
E-Artikel |
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| Erscheinungsjahr: |
2024 |
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| Erschienen: |
2024 |
| Enthalten in: |
Zur Gesamtaufnahme - volume:24 |
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| Enthalten in: |
BMC public health - 24(2024), 1 vom: 09. Feb. |
| Sprache: |
Englisch |
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| Beteiligte Personen: |
He, Kaiyu [VerfasserIn] |
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| Links: |
Volltext [kostenfrei] |
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| Themen: |
COVID-19 |
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| Anmerkungen: |
© The Author(s) 2024 |
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| doi: |
10.1186/s12889-024-17920-4 |
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| funding: |
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| Förderinstitution / Projekttitel: |
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| PPN (Katalog-ID): |
SPR054697182 |
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| 520 | |a Importance Contact tracing is the process of identifying people who have recently been in contact with someone diagnosed with an infectious disease. During an outbreak, data collected from contact tracing can inform interventions to reduce the spread of infectious diseases. Understanding factors associated with completion rates of contact tracing surveys can help design improved interview protocols for ongoing and future programs. Objective To identify factors associated with completion rates of COVID-19 contact tracing surveys in New York City (NYC) and evaluate the utility of a predictive model to improve completion rates, we analyze laboratory-confirmed and probable COVID-19 cases and their self-reported contacts in NYC from October 1st 2020 to May 10th 2021. Methods We analyzed 742,807 case investigation calls made during the study period. Using a log-binomial regression model, we examined the impact of age, time of day of phone call, and zip code-level demographic and socioeconomic factors on interview completion rates. We further developed a random forest model to predict the best phone call time and performed a counterfactual analysis to evaluate the change of completion rates if the predicative model were used. Results The percentage of contact tracing surveys that were completed was 79.4%, with substantial variations across ZIP code areas. Using a log-binomial regression model, we found that the age of index case (an individual who has tested positive through PCR or antigen testing and is thus subjected to a case investigation) had a significant effect on the completion of case investigation – compared with young adults (the reference group,24 years old < age < = 65 years old), the completion rate for seniors (age > 65 years old) were lower by 12.1% (95%CI: 11.1% – 13.3%), and the completion rate for youth group (age < = 24 years old) were lower by 1.6% (95%CI: 0.6% –2.6%). In addition, phone calls made from 6 to 9 pm had a 4.1% (95% CI: 1.8% – 6.3%) higher completion rate compared with the reference group of phone calls attempted from 12 and 3 pm. We further used a random forest algorithm to assess its potential utility for selecting the time of day of phone call. In counterfactual simulations, the overall completion rate in NYC was marginally improved by 1.2%; however, certain ZIP code areas had improvements up to 7.8%. Conclusion These findings suggest that age and time of day of phone call were associated with completion rates of case investigations. It is possible to develop predictive models to estimate better phone call time for improving completion rates in certain communities. | ||
| 650 | 4 | |a COVID-19 |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Contact tracing surveys |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Survey completion rates |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Random forest algorithm |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Log-binomial regression |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Model prediction |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Foerster, Steffen |4 aut | |
| 700 | 1 | |a Vora, Neil M. |4 aut | |
| 700 | 1 | |a Blaney, Kathleen |4 aut | |
| 700 | 1 | |a Keeley, Chris |4 aut | |
| 700 | 1 | |a Hendricks, Lisa |4 aut | |
| 700 | 1 | |a Varma, Jay K. |4 aut | |
| 700 | 1 | |a Long, Theodore |4 aut | |
| 700 | 1 | |a Shaman, Jeffrey |4 aut | |
| 700 | 1 | |a Pei, Sen |4 aut | |
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