OBJECTIVES: The purpose of this study was to assess the impact of recent influenza epidemics on mortality in the United States and to develop an index for comparing the severity of individual epidemics. METHODS: A cyclical regression model was applied to weekly national vital statistics from 1972 through 1992 to estimate excesses in pneumonia and influenza mortality and all-cause mortality for each influenza season. Each season was categorized on the basis of increments of 2000 pneumonia and influenza excess deaths, and each of these severity categories was correlated with a range of all-cause excess mortality. RESULTS: Each of the 20 influenza seasons studied was associated with an average of 5600 pneumonia and influenza excess deaths (range, 0-11,800) and 21,300 all-cause excess deaths (range, 0-47,200). Most influenza A(H3N2) seasons fell into severity categories 4 to 6 (23,000-45,000 all-cause excess deaths), whereas most A(H1N1) and B seasons were ranked in categories 1 to 3 (0-23,000 such deaths). CONCLUSIONS: From 1972 through 1992, influenza epidemics accounted for a total of 426,000 deaths in the United States, many times more than those associated with recent pandemics. The influenza epidemic severity index was useful for categorizing severity and provided improved seasonal estimates of the total number of influenza-related deaths.

The detection of unusual patterns in the occurrence of diseases and other health events presents an important challenge to public health surveillance. This paper discusses three analytic methods for identifying aberrations in underlying distributions. The methods are illustrated on selected infectious diseases included in the National Notifiable Diseases Surveillance System of the Centers for Disease Control. Results suggest the utility of such an analytic approach. Further work will determine the sensitivity of such methods to variations in the occurrence of disease. These methods are useful for evaluating and monitoring public health surveillance data.

To determine the relative merits of two quantitative methods used to estimate the summary effects of observational studies, the authors compared two methods of meta-analysis. Each quantified the relation between oral contraceptive use and the risk for ovarian cancer. One analysis consisted of a meta-analysis using summary data from 11 published studies from the literature (MAL) in which the study was the unit of analysis, and the second consisted of a meta-analysis using individual patient data (MAP) in which the patient was the unit of analysis. The authors found excellent quantitative agreement between the summary effect estimates from the MAL and the MAP. The MAP permits analysis 1) among outcomes, exposures, and confounders not investigated in the original studies, 2) when the original effect measures differ among studies and cannot be converted to a common measure (e.g., slopes vs. correlation coefficients), and 3) when there is a paucity of studies. The MAL permits analysis 1) when resources are limited, 2) when time is limited, and 3) when original study data are not available or are available only from a biased sample of studies. In public health epidemiology, data from original studies are often accessible only to limited numbers of research groups and for only a few types of studies that have high public health priority. Consequently, few opportunities for pooled analysis exist. However, from a policy view, MAL will provide answers to many questions and will help in identifying questions for future investigation.

One goal of a public health surveillance system is to provide a reliable forecast of epidemiological time series. This paper describes a study that used data collected through a national public health surveillance system in the United States to evaluate and compare the performances of a seasonal autoregressive integrated moving average (SARIMA) and a dynamic linear model (DLM) for estimating case occurrence of two notifiable diseases. The comparison uses reported cases of malaria and hepatitis A from January 1980 to June 1995 for the United States. The residuals for both predictor models show that they were adequate tools for use in epidemiological surveillance. Qualitative aspects were considered for both models to improve the comparison of their usefulness in public health. Our comparison found that the two forecasting modelling techniques (SARIMA and DLM) are comparable when long historical data are available (at least 52 reporting periods). However, the DLM approach has some advantages, such as being more easily applied to different types of time series and not requiring a new cycle of identification and modelling when new data become available.

Influenza-associated mortality has traditionally been estimated as the excess mortality above a baseline of deaths during influenza epidemic periods. Excess mortality estimates are not timely, because national vital statistics data become available after a period of 2-3 years. To develop a method for timely reporting, we used the 121 Cities Surveillance System (121 Cities), maintained at the Centers for Disease Control and Prevention, as an alternative data source. We fit a cyclical regression model to time series of weekly 121 Cities pneumonia and influenza deaths for 1972-1996 to estimate the excess pneumonia and influenza mortality and to compare these figures with national vital statistics estimates for 20 influenza seasons during 1972-1992. Seasonal excess mortality based on 121 Cities correlated well with the national data: for 18 (90%) of 20 seasons, our influenza epidemic severity index category approximated the result based on national vital statistics. We generated preliminary severity categories for the four recent seasons during 1992-1996. We conclude that the 121 Cities Surveillance System can be used for the timely assessment of the severity of future influenza epidemics and pandemics. Timely pneumonia and influenza mortality reporting systems established in sentinel countries worldwide would help alert public health officials and allow prompt prevention and intervention strategies during future influenza epidemics and pandemics.