Literature DB >> 8111516

Do family physicians make good sentinels for influenza?

J Buffington1, L E Chapman, L M Schmeltz, A P Kendal.   

Abstract

OBJECTIVE: To determine whether volunteer family physician reports of the frequency of influenza-like illness (ILI) usefully supplement information from other influenza surveillance systems conducted by the Centers for Disease Control and Prevention.
DESIGN: Evaluation of physician reports from five influenza surveillance seasons (1987-88 through 1991-92).
SETTING: Family physician office practices in all regions of the United States. PARTICIPANTS: An average of 140 physicians during each of five influenza seasons.
INTERVENTIONS: None. OUTCOME MEASURES: An office visit or hospitalization of a patient for ILI, defined as presence of fever (temperature > or = 37.8 degrees C) and cough, sore throat, or myalgia, along with the physician's clinical judgment of influenza. A subset of physicians collected specimens for confirmation of influenza virus by culture.
RESULTS: Physicians attributed 81,408 (5%) of 1,672,542 office visits to ILI; 2754 (3%) patients with ILI were hospitalized. Persons 65 years of age and older accounted for 11% of visits for ILI and 43% of hospitalizations for ILI. In three of five seasons, physicians obtained influenza virus isolates from a greater proportion of specimens compared with those processed by World Health Organization laboratories (36% vs 12%). Influenza virus isolates from sentinel physicians peaked from 1 to 4 weeks earlier than those reported by World Health Organization laboratories. Physicians reported peak morbidity 1 to 4 weeks earlier than state and territorial health departments in four of five seasons and 2 to 5 weeks earlier than peak mortality reported by 121 cities during seasons with excess mortality associated with pneumonia and influenza.
CONCLUSIONS: Family physicians provide sensitive, timely, and accurate community influenza morbidity data that complement data from other surveillance systems. This information enables monitoring of the type, timing, and intensity of influenza activity and can help health care workers implement prevention or control measures.

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Year:  1993        PMID: 8111516     DOI: 10.1001/archfami.2.8.859

Source DB:  PubMed          Journal:  Arch Fam Med        ISSN: 1063-3987


  7 in total

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Authors:  James W Keck; John T Redd; James E Cheek; Larry J Layne; Amy V Groom; Sassa Kitka; Michael G Bruce; Anil Suryaprasad; Nancy L Amerson; Theresa Cullen; Ralph T Bryan; Thomas W Hennessy
Journal:  J Am Med Inform Assoc       Date:  2013-06-06       Impact factor: 4.497

2.  Influenza A and B epidemic criteria based on time-series analysis of health services surveillance data.

Authors:  P Quénel; W Dab
Journal:  Eur J Epidemiol       Date:  1998-04       Impact factor: 8.082

3.  Integrating General Practice Into the Australian COVID-19 Response: A Description of the General Practitioner Respiratory Clinic Program in Australia.

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Journal:  Ann Fam Med       Date:  2022 May-Jun       Impact factor: 5.707

4.  Type- and subtype-specific detection of influenza viruses in clinical specimens by rapid culture assay.

Authors:  T Ziegler; H Hall; A Sánchez-Fauquier; W C Gamble; N J Cox
Journal:  J Clin Microbiol       Date:  1995-02       Impact factor: 5.948

5.  Recruiting primary care clinicians for public health and bioterrorism surveillance.

Authors:  Jonathan L Temte; Michael E Grasmick
Journal:  WMJ       Date:  2009-04

6.  Influenza surveillance: experiences from establishing a sentinel surveillance system in Germany.

Authors:  J Szecsenyi; H Uphoff; S Ley; H D Brede
Journal:  J Epidemiol Community Health       Date:  1995-08       Impact factor: 3.710

7.  Using influenza surveillance networks to estimate state-specific prevalence of SARS-CoV-2 in the United States.

Authors:  Justin D Silverman; Nathaniel Hupert; Alex D Washburne
Journal:  Sci Transl Med       Date:  2020-06-22       Impact factor: 17.956

  7 in total

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