Influenza and the winter increase in mortality in the United States, 1959-1999

Entropy Research Institute, 262 West Saddle River Road, Upper Saddle River, NJ 07458, USA.
American Journal of Epidemiology (Impact Factor: 4.98). 10/2004; 160(5):492-502. DOI: 10.1093/aje/kwh227
Source: PubMed

ABSTRACT In economically developed countries, mortality increases distinctly during winter. Many causes have been suggested, including light-dark cycles, temperature/weather, and infectious agents. The authors analyzed monthly mortality in the United States during the period 1959-1999 for four major disease classes. The authors isolated the seasonal component of mortality by removing trends and standardizing the time series. They evaluated four properties: coincidence in mortality peaks, autocorrelation structure and autoregressive integrated moving average (ARIMA) models, magnitude, and age distribution. Peak months of mortality for ischemic heart disease, cerebrovascular disease, and diabetes mellitus coincided appropriately with peaks in pneumonia and influenza, and coefficients of autocorrelation and ARIMA models were essentially indistinguishable. The magnitude of the seasonal component was highly correlated with traditional measures of excess mortality and was significantly larger in seasons dominated by influenza A(H2N2) and A(H3N2) viruses than in seasons dominated by A(H1N1) or B viruses. There was an age shift in mortality during and after the 1968/69 pandemic in each disease class, with features specific to influenza A(H3N2). These findings suggest that the cause of the winter increase in US mortality is singular and probably influenza. Weather and other factors may determine the timing and modulate the magnitude of the winter-season increase in mortality, but the primary determinant appears to be the influenza virus.


Available from: David Fedson, Jun 03, 2015
  • Source
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Tuberculosis is a major global public health problem, which also affects economic and social development. China has the second largest burden of tuberculosis in the world. The tuberculosis morbidity in Xinjiang is much higher than the national situation; therefore, there is an urgent need for monitoring and predicting tuberculosis morbidity so as to make the control of tuberculosis more effective. Recently, the Box-Jenkins approach, specifically the autoregressive integrated moving average (ARIMA) model, is typically applied to predict the morbidity of infectious diseases; it can take into account changing trends, periodic changes, and random disturbances in time series. Autoregressive conditional heteroscedasticity (ARCH) models are the prevalent tools used to deal with time series heteroscedasticity. In this study, based on the data of the tuberculosis morbidity from January 2004 to June 2014 in Xinjiang, we establish the single ARIMA (1, 1, 2) (1, 1, 1)12 model and the combined ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model, which can be used to predict the tuberculosis morbidity successfully in Xinjiang. Comparative analyses show that the combined model is more effective. To the best of our knowledge, this is the first study to establish the ARIMA model and ARIMA-ARCH model for prediction and monitoring the monthly morbidity of tuberculosis in Xinjiang. Based on the results of this study, the ARIMA (1, 1, 2) (1, 1, 1)12-ARCH (1) model is suggested to give tuberculosis surveillance by providing estimates on tuberculosis morbidity trends in Xinjiang, China.
    PLoS ONE 03/2015; 10(3):e0116832. DOI:10.1371/journal.pone.0116832 · 3.53 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: Background Mathematical or statistical tools are capable to provide a valid help to improve surveillance systems for healthcare and non-healthcare-associated bacterial infections. The aim of this work is to evaluate the time-varying auto-adaptive (TVA) algorithm-based use of clinical microbiology laboratory database to forecast medically important drug-resistant bacterial infections.Methods Using TVA algorithm, six distinct time series were modelled, each one representing the number of episodes per single `ESKAPE¿ ( E nterococcus faecium, S taphylococcus aureus, K lebsiella pneumoniae, A cinetobacter baumannii, P seudomonas aeruginosa and E nterobacter species) infecting pathogen, that had occurred monthly between 2002 and 2011 calendar years at the Università Cattolica del Sacro Cuore general hospital.ResultsMonthly moving averaged numbers of observed and forecasted ESKAPE infectious episodes were found to show a complete overlapping of their respective smoothed time series curves. Overall good forecast accuracy was observed, with percentages ranging from 82.14% for E. faecium infections to 90.36% for S. aureus infections.Conclusions Our approach may regularly provide physicians with forecasted bacterial infection rates to alert them about the spread of antibiotic-resistant bacterial species, especially when clinical microbiological results of patients¿ specimens are delayed.
    BMC Infectious Diseases 12/2014; 14(1):634. DOI:10.1186/s12879-014-0634-9 · 2.56 Impact Factor

Similar Publications