Lipscomb, Rheana
(2021)
The Classification of COVID-19 Mortality and Evaluation of Deceased Characteristics by Identification Method in Allegheny County, Pennsylvania.
Master Essay, University of Pittsburgh.
Abstract
COVID-19 mortality data are an important component of measuring the impact of the pandemic and can be used to identify disproportionately affected populations. Insights from COVID-19 case and mortality data are used to target interventions and mitigation strategies to prevent further COVID-19 related mortality. Passive surveillance systems used to identify COVID-19 mortality are limited by underreporting and timeliness (i.e., lag in reporting), which can result in an underestimation of COVID-19 mortality. The public health workforce has been overwhelmed by effects of the COVID-19 pandemic, and most local health departments have not yet had the opportunity to identify COVID-19 deaths missed by their surveillance systems. This descriptive analysis identified COVID-19 related deaths in Allegheny County between March 14, 2020 and February 19, 2021 in Allegheny County, Pennsylvania. Deceased characteristics were compared between deaths identified by the Allegheny County Health Department (ACHD) COVID-19 mortality surveillance and deaths not identified by ACHD surveillance, identified as part of the 2021 ACHD Pittsburgh Summer Institute. A multivariate logistic regression model was used to assess if age, sex, race/ethnicity, and vulnerability index independently predicted if COVID-19 deaths were identified by ACHD surveillance. Most COVID-19 deaths were identified by ACHD COVID-19 surveillance: of 1,462 total COVID-19 deaths, 1,325 (91%) deaths were identified through ACHD COVID-19 mortality surveillance, and 137 (9%) deaths were identified outside of ACHD COVID-19 mortality surveillance. The odds of being female sex (OR 1.52) and race other than white or black (OR 3.49) were significantly higher among deaths not identified by ACHD surveillance, when controlling for age and vulnerability index, which were not related to identification method. ACHD COVID-19 mortality surveillance from March 14, 2020 to February 19, 2021 was largely accurate and indiscriminate and is likely subject to the general limitations of passive surveillance systems. The public health importance of these findings is that COVID-19 deaths have been identified outside of traditional surveillance methods, and these deaths differ from the deaths identified by passive surveillance regarding sex and race/ethnicity, although these differences are small.
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Details
| Item Type: |
Other Thesis, Dissertation, or Long Paper
(Master Essay)
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| Contributors: |
| Contribution | Contributors Name | Email | Pitt Username | ORCID  |
|---|
| Committee Chair | King, Wendy | kingw@edc.pitt.edu | kingw | UNSPECIFIED | | Committee Member | Youk, Ada | ayouk@pitt.edu | ayouk | UNSPECIFIED | | Committee Member | Bertolet, Marianne | mhb12@pitt.edu | mhb12 | UNSPECIFIED | | Committee Member | Fiddner, Jennifer | jennifer.fiddner@alleghenycounty.us | UNSPECIFIED | UNSPECIFIED |
|
| Date: |
19 November 2021 |
| Date Type: |
Completion |
| Submission Date: |
13 December 2021 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
52 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Epidemiology |
| Degree: |
MPH - Master of Public Health |
| Thesis Type: |
Master Essay |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
COVID-19, Pennsylvania, Surveillance, COVID-19 Mortality, Logistic Regression |
| Date Deposited: |
06 Jan 2022 19:17 |
| Last Modified: |
06 Jan 2024 06:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/42086 |
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