scholarly journals Predicting virologically confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007‐2015 influenza seasons

2021 ◽  
Author(s):  
Talia M. Quandelacy ◽  
Shanta Zimmer ◽  
Justin Lessler ◽  
Charles Vukotich ◽  
Rachel Bieltz ◽  
...  
Keyword(s):  
Allegheny County ◽  
School Absences

scholarly journals Predicting virologically-confirmed influenza using school absences in Allegheny County, Pennsylvania, USA during the 2007-2015 influenza seasons

2021 ◽  
Author(s):  
Talia Quandelacy ◽  
Shanta Zimmer ◽  
Justin Lessler ◽  
Charles VUKOTICH ◽  
Rachel Bieltz ◽  
...  
Keyword(s):  
School Districts ◽  
Negative Binomial ◽  
Negative Binomial Regression ◽  
Absolute Error ◽  
Health Department ◽  
Influenza Surveillance ◽  
Allegheny County ◽  
Seasonal Model ◽  
School Absences ◽  
One Year

Background Children are important in community-level influenza transmission. School-based monitoring may inform influenza surveillance. Methods We used reported weekly confirmed influenza in Allegheny County during the 2007, and 2010-2015 influenza seasons using Pennsylvania’s Allegheny County Health Department all-age influenza cases from health facilities, and all-cause and influenza-like illness (ILI)-specific absences from nine county school districts. Negative binomial regression predicted influenza cases using all-cause and illness-specific absence rates, calendar week, average weekly temperature and relative humidity, using four cross-validations. Results School districts reported 2,184,220 all-cause absences (2010-2015). Three one-season studies reported 19,577 all-cause and 3,012 ILI-related absences (2007, 2012, 2015). Over seven seasons, 11,946 confirmed influenza cases were reported. Absences improved seasonal model fits and predictions. Multivariate models using elementary school absences outperformed middle and high school models (relative mean absolute error (relMAE)=0.94, 0.98, 0.99). K-5 grade-specific absence models had lowest mean absolute errors (MAE) in cross-validations. ILI-specific absences performed marginally better than all-cause absences in two years, adjusting for other covariates, but markedly worse one year. Conclusions Our findings suggest seasonal models including K-5th grade absences predict all-age confirmed influenza and may serve as a useful surveillance tool.

Incidence of ESRD and survival after renal replacement therapy in patients with type 1 diabetes: a report from the allegheny county registry

2003 ◽  
Vol 42 (1) ◽  
pp. 117-124 ◽  
Author(s):  
Rimei Nishimura ◽  
Janice S Dorman ◽  
Zsolt Bosnyak ◽  
Naoko Tajima ◽  
Dorothy J Becker ◽  
...  
Keyword(s):  
Type 1 Diabetes ◽  
Renal Replacement Therapy ◽  
Replacement Therapy ◽  
Allegheny County

TNF-308 Modifies the Effect of Second-hand Smoke on Respiratory Illness-Related School Absences

PEDIATRICS ◽  
2007 ◽  
Vol 120 (Supplement 3) ◽  
pp. S111.2-S112
Author(s):  
Allison M. Hofmann ◽  
Larry W. Williams
Keyword(s):  
Respiratory Illness ◽  
Second Hand Smoke ◽  
School Absences

Do Slum Upgrading Programs Impact School Attendance?

2021 ◽  
Author(s):  
Wladimir Zanoni ◽  
Paloma Acevedo ◽  
Diego Guerrero
Keyword(s):  
Elementary School ◽  
Education Policy ◽  
Regression Discontinuity ◽  
School Attendance ◽  
Policy Implications ◽  
Fuzzy Regression ◽  
School Absences ◽  
Slum Upgrading

This paper analyzes how slum upgrading programs impact elementary school childrens attendance in Uruguay. We take advantage of the eligibility rule that deems slums eligible for a SUP program if they have 40 or more dwelling units. Using a fuzzy regression discontinuity estimator, we find that students exposed to SUPs are 17 percent less likely to be at the 90th percentile of the yearly count of school absences. That effect appears to be driven by how SUPs impact girls. These interventions have effects that last for more than five years after their implementation. We discuss some critical urban and education policy implications of our findings.

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