scispace - formally typeset
Search or ask a question
Author

Guzman Ruiz Yf

Bio: Guzman Ruiz Yf is an academic researcher from University of Washington. The author has contributed to research in topics: Public health. The author has an hindex of 1, co-authored 1 publications receiving 1 citations.
Topics: Public health

Papers
More filters
Posted ContentDOI
11 Apr 2021-medRxiv
Abstract: BackgroundIn the context of the COVID-19 pandemic, public health teams have struggled to conduct monitoring for confirmed or suspicious COVID-19 patients. However, monitoring these patients is critical to improving the chances of survival, and therefore, a prioritization strategy for these patients is warranted. This study developed a monitoring algorithm for COVID-19 patients for the Colombian Ministry of Health and Social Protection (MOH). MethodsThis work included 1) a literature review, 2) consultations with MOH and National Institute of Health officials, and 3) data analysis of all positive COVID-19 cases and their outcomes. We used clinical and socioeconomic variables to develop a set of risk categories to identify severe cases of COVID-19. ResultsThis tool provided four different risk categories for COVID-19 patients. As soon as the time of diagnosis, this tool can identify 91% of all severe and fatal COVID-19 cases within the first two risk categories. ConclusionThis tool is a low-cost strategy to prioritize patients at higher risk of experiencing severe COVID-19. This tool was developed so public health teams can focus their scarce monitoring resources on individuals at higher mortality risk. This tool can be easily adapted to the context of other lower and middle-income countries. Policymakers would benefit from this low-cost strategy to reduce COVID-19 mortality, particularly during outbreaks.

1 citations


Cited by
More filters
DOI
01 Nov 2021
TL;DR: In this paper, the authors developed a Markov simulation model of COVID-19 infection combined with a Susceptible-Infected-Recovered structure to estimate the incremental cost-effectiveness of a comprehensive TTI strategy compared to no intervention over a one-year horizon, from both the health system and the societal perspective.
Abstract: Summary Background During the COVID-19 pandemic, Test-Trace-Isolate (TTI) programs have been recommended as a risk mitigation strategy. However, many governments have hesitated to implement them due to their costs. This study aims to estimate the cost-effectiveness of implementing a national TTI program to reduce the number of severe and fatal cases of COVID-19 in Colombia. Methods We developed a Markov simulation model of COVID-19 infection combined with a Susceptible-Infected-Recovered structure. We estimated the incremental cost-effectiveness of a comprehensive TTI strategy compared to no intervention over a one-year horizon, from both the health system and the societal perspective. Hospitalization and mortality rates were retrieved from Colombian surveillance data. We included program costs of TTI intervention, health services utilization, PCR diagnosis test, productivity loss, and government social program costs. We used the number of deaths and quality-adjusted life years (QALYs) as health outcomes. Sensitivity analyses were performed. Findings Compared with no intervention, the TTI strategy reduces COVID-19 mortality by 67%. In addition, the program saves an average of $1,045 and $850 per case when observed from the social and the health system perspective, respectively. These savings are equivalent to two times the current health expenditures in Colombia per year. Interpretation The TTI program is a highly cost-effective public health intervention to reduce the burden of COVID-19 in Colombia. TTI programs depend on their successful and speedy implementation. Funding This study was supported by the Colombian Ministry of Health through award number PUJ-04519-20 received by EPQ AVO and SDS declined to receive any funding support for this study. The contents are the responsibility of all the individual authors.

6 citations