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Steps in Outbreak Investigation01:18

Steps in Outbreak Investigation

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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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Censoring Survival Data01:09

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Survival analysis is a statistical method used to analyze time-to-event data, often employed in fields such as medicine, engineering, and social sciences. One of the key challenges in survival analysis is dealing with incomplete data, a phenomenon known as "censoring." Censoring occurs when the event of interest (such as death, relapse, or system failure) has not occurred for some individuals by the end of the study period or is otherwise unobservable, and it might have many different...
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Assumptions of Survival Analysis01:15

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Survival models analyze the time until one or more events occur, such as death in biological organisms or failure in mechanical systems. These models are widely used across fields like medicine, biology, engineering, and public health to study time-to-event phenomena. To ensure accurate results, survival analysis relies on key assumptions and careful study design.
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Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and...
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Residuals and Least-Squares Property01:11

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The vertical distance between the actual value of y and the estimated value of y. In other words, it measures the vertical distance between the actual data point and the predicted point on the line
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Assessing SARS-CoV-2 Testing Adherence in a University Town: Recurrent Event Modeling Analysis.

Yury E García1, Alec J Schmidt1, Leslie Solis2

  • 1Department of Public Health Sciences, University of California, Davis, CA, United States.

JMIR Public Health and Surveillance
|April 17, 2024
PubMed
Summary

Community COVID-19 testing in Davis, California, showed high participation across diverse groups. Demographic factors influenced testing adherence, with younger adults and certain ethnic minorities less likely to consistently test.

Keywords:
COVID-19COVID-19 surveillance programHDT: HYTHealthy Davis TogetherHealthy Yolo TogetherPCRRT-qPCRSARS-CoV-2USAUnited Statesadherencecommunity basedcommunity surveillancecompliancecoronavirusdemographicdemographicsengagementinfection controlinfectiousparticipationpolymerase chain reactionpublic healthresponse programresponse programsreverse transcription quantitative polymerase chain reactionsevere acute respiratory syndrome coronavirus 2surveillancetestingtrendtrendsviralvirusviruses

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Area of Science:

  • Public Health
  • Epidemiology
  • Community Health Initiatives

Background:

  • The Healthy Davis Together program aimed to control COVID-19 spread and restore normalcy through interventions like free asymptomatic testing.
  • This initiative provided a platform for community-wide pandemic response in Davis, California.

Purpose of the Study:

  • To identify demographic characteristics of individuals participating in a community COVID-19 testing program.
  • To assess adherence to COVID-19 testing over time within a diverse population.
  • To analyze testing patterns in relation to dominant COVID-19 variants (Delta and Omicron).

Main Methods:

  • Analysis of testing data from November 2020 to June 2022, covering 770,165 tests among 89,924 residents.
  • Utilized a recurrent model to explore testing frequency, timing, and demographic influences.
  • Compared testing behaviors across different demographic groups, including age, gender, and ethnicity.

Main Results:

  • High overall participation (41.1% of Yolo County population) with significant engagement from diverse racial/ethnic and age groups.
  • Peak testing and positive cases occurred during the Omicron variant period.
  • Identified disparities in testing adherence: younger adults (20-34) and Hispanic/Latino and Black/African American individuals showed lower adherence compared to older adults and White individuals, respectively. Men were also less likely to retest than women.

Conclusions:

  • Community-wide free asymptomatic testing programs are valuable for understanding testing adherence and trends.
  • Demographic factors significantly influence participation in public health testing initiatives.
  • Findings offer insights for optimizing future pandemic response and testing strategies.