Perspectives on Epidemiologic Surveillance in the 21st Century

Abstract

This paper describes the importance of epidemiologic surveillance as a systematic, ongoing and population-based system for early warning and program development in the 21st century. Such a system routinely collects data on three classes of indicators (health outcomes, risk factors and intervention strategies) to set up both an early warning system (to identify associations and make predictions on health outcomes) and a program development system (to assess the need for intervention strategies, to plan and implement such strategies and to assess their effectiveness). A comprehensive surveillance system must be systematic (evidence-based selection of indicators, not hypothesis-driven), ongoing (continuous data collection, including repeated surveys) and population-based (whole population, or representative samples of the population). Such a system need not be developed from scratch, but can be based on linkage of existing databases and collection of additional information for identified data gaps. The initial steps for selecting indicators and creating a prototype framework for a comprehensive surveillance system are proposed to stimulate further discussion. It is suggested that surveillance systems should be used more widely in public health.

Key words: control; epidemiology; health surveillance; prevention; program evaluation; risk factors

Introduction

Epidemiologic surveillance dates back to the time of John Graunt, who published the Natural and Political Observations Made Upon the Bills of Mortality in 1662.¹ Graunt's approach for the analysis of death certificates (Bills of Mortality), that volumes of data should be reduced to a few tables and that profit may be gained by analyzing these tables, is consistent with the modern technique of population-based epidemiologic surveillance.² In the subsequent 300 years, however, the focus of health research shifted to sample-based studies: cross-sectional, cohort and case-control studies, and clinical trials.^3-6

In recent decades, awareness of the limitations of sample-based epidemiologic studies has grown,⁷ along with recognition of the importance of population-based surveillance systems for measuring the health status of a population,⁸ for early warning of emerging health risks and for program development.⁹ Systematic and timely analysis of health trends has been identified as increasingly important for evidence-based policy and program development.^10,11 At the same time, biophysical and socio-economic data have become invaluable in the understanding of relationships among human health, risk factors and interventions.¹²

It appears that epidemiologic surveillance may come back full circle in the 21st century and become once again the focus of health research. (It has been suggested that epidemiologic surveillance is not research per se, and can therefore only be called public health surveillance.¹³ But it is my opinion that health research can be conducted in the next century using well-maintained and well-validated surveillance databases.)

This paper offers a point of view for debate on the blueprints for a systematic, ongoing and population-based surveillance system for the 21st century, with the hope of stimulating further discussion on this very important topic.

Problems of Sample-based Studies

In a sample-based approach, investigators conduct custom-designed and mostly localized studies to investigate associations. However, these studies are prone to biases, including those that Sackett¹⁴ called "positive results bias," "hot topic bias," "wrong sample size bias," "expectation bias" and "data dredging bias," and a number of biases reviewed elsewhere by this author.^15,16 (Population-based studies are also susceptible to these biases, but to a lesser extent than sample-based studies.)

Consider a situation where an exposure and a disease are not associated. Due to problems in study design, data collection or analysis, or by chance, a sample-based study may incorrectly determine that there is an association. When published, this false positive study can create a hot topic bias, that is, more investigators will become interested in the topic.

In this case, let's say 100 studies may be designed. If the type I error rate (significance level) of these studies is chosen at the conventional 0.05, then on average, 5 of the 100 studies will show false positive results. Since positive results are more likely to be submitted by investigators to scientific journals (positive results bias) and accepted by editors (editor's bias), this will lead to an even bigger hot topic bias, and another false positive research cycle will begin. (In the case of a hot topic, it is unlikely that all of the 95 correctly negative studies will go unpublished, and letters to the editor will almost certainly follow. Although the hot topic bias could thus be somewhat self-corrected, the overall tendency for the bias remains.)

Through this biased process, an investigator can almost always "prove" something out of nothing. Results of such an unsystematic, non-population-based approach would likely focus efforts in certain narrow areas, ignoring some other major and real issues. While sample-based studies have provided a tremendous amount of knowledge, their limitations due to false positive research cycles must be recognized.

Population-based Surveillance

Early work on population-based health surveillance included cluster studies and ecologic correlation studies. In the former type of study, observation of unusual clusters of cases of a rare disease in population subgroups exposed to a common risk factor may lead to hazard identification. Ecologic correlation studies, on the other hand, seek to establish an association between exposure and disease occurrence by comparing disease rates among populations in different geographic areas subject to varying levels of exposure.¹⁷

An example of an ecologic correlation study that is very close to a surveillance system is the correlation study by Murata et al.¹⁸ These researchers looked at how mortality and incidence rates of cancers of the lung, colon and rectum correlated with 63 environmental variables (including population density, number of households living on welfare aid, number of hospitals, tobacco tax revenue, sulphur dioxide level, rainfall, traffic density and meat consumption) in 583 geographic districts of Japan from 1975 to 1979. However, like other similar studies, this study failed as a surveillance system because it was not performed on a long-term basis, results were not used in the systematic development of intervention strategies and there was no systematic information dissemination. Furthermore, cluster studies and ecologic correlation studies to date are susceptible to ecologic fallacies and other potential problems.¹⁶

The US Centers for Disease Control and Prevention (CDC) defined epidemiologic surveillance as the "ongoing systematic collection, analysis, and interpretation of health data essential to the planning, implementation, and evaluation of public health practice, closely integrated with the timely dissemination of these data to those who need to know."¹⁹ More recently, Monson pointed out that, as scientists of the 21st century, "rather than conducting short-term studies that will test a hypothesis, we must be developing systems to collect data on exposure and disease that will become part of the fabric of the community, the workplace, and the health care organization."²⁰ The importance of a comprehensive, systematic, long-term and population-based surveillance system has been stressed.

Recent textbooks by Halperin and Baker² and Teutsch and Churchill¹³ have summarized the basic principles of public health surveillance. However, the form, content and operating principles of a comprehensive epidemiologic surveillance system for the 21st century have not yet been described in the literature.

A Vision for Comprehensive Epidemiologic Surveillance for the 21st Century

In a comprehensive and systematic approach to surveillance, all associations with the same strength will have an equal chance of being detected. In the 21st century, scientists and public health officials should therefore concentrate more on systematic, ongoing and population-based data collection systems than on hypothesis-driven, short-term sample-based data collection. Important and useful data on health indicators, risk indicators and intervention indicators should be collected routinely, systematically and accurately for the community. (Many of the current databases may be routine and population-based, but neither systematic nor accurate.) These databases can then be analyzed systematically to monitor trends of health, risk and intervention in the community, to identify emerging health risks (early warning system) and to develop and evaluate evidence-based disease control and prevention programs (program development system).

To develop a comprehensive early warning and program development system based on surveillance, the following three questions need to be answered.

1. What indicator variables should be included in the surveillance system?

A conceptual framework for health information, the Health Template, was put forward in 1991 by the National Task Force on Health Information²¹ (Figure 1). The Health Template classifies health information into three major areas-individual characteristics, external milieu and "health-affecting" interventions-with further subdivision into several levels of categories. The Health Template attempts to provide a framework for the subsets of quantitative information and a basis for possible development of forecasting and policy modelling. Therefore, it can be used potentially as a model for the selection of indicators for a comprehensive surveillance system.

The problem with the Health Template, however, is that it is too ambitious. In the current version there are 44 categories for individual characteristics, 93 for external milieu and 59 for health-affecting interventions, a total of 196. Most of these categories are described in general terms, such as "air" or "social support," without working definitions. When fully developed, the whole classification scheme of the Health Template may have too many variables for a practical surveillance system.

The list of indicator variables must be narrowed down to a manageable size for use in a comprehensive surveillance system. Several techniques may be used to select indicator variables, including literature research, Delphi surveys among experts^22-24 and experts' consensus workshops.²² These techniques will involve development of a priori selection criteria.


	Last Updated: 2002-10-22		Important Notices