Under the Weather Climate, Ecosystems, and Infectious Disease (2001) / Chapter Skim
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Analytical Approaches for Studying Climate/Disease Linkages
Analytical Approaches for Studying Climate/Disease Linkages
Pages 59-79
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From page 59... ...
OBSERVATIONAL AND EXPERIMENTAL STUDIES Observational studies of the relationship between climate variations and health outcomes provide the foundation for developing theory that is used in models and, ultimately, for making forecasts about future impacts associated with climatic changes. As described in the following sections, observational studies can include retrospective and prospective analysis of natural variations, retrospective analysis of historical trends, and interregional comparisons.
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From page 60... ...
In general, though, this analytical approach does hold potential for improving forecasts of how short-term variability may alter epidemic risk; and if consistent relationships are found over a long time period or in many different places, more confidence can be gained in using these relationships to forecast future changes. Prospective Observations of Natural Variations Under some circumstances, surveillance of diseases may be ongoing during periods of anomalous weather events, thus allowing for "prospective" comparison of patterns of variability in disease incidence and climate.
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From page 61... ...
These are all reasons why strengthened surveillance of disease incidence is critical to our capacity to analyze future health impacts from climate variability. Retrospective Analysis of Historical Trends This approach is similar to that involving retrospective analysis of natural variation, except that it compares the trends or slopes of change during the period of observation.
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From page 62... ...
They can help elucidate parts of a causal pathway and provide information that may be used in combination with other observations or to quantify particular associations.
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From page 63... ...
They can be quite valuable, however, in setting parameter values for some processes and in developing hypotheses that are better tested through other approaches. MATHEMATICAL MODELING Mathematical modeling can be a powerful tool for gaining insights into the dynamics of infectious disease epidemics, integrating information from laboratory and field studies, providing direction for future experimental and observational studies, evaluating monitoring and control strategies, and making predictions about future disease risk.
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From page 64... ...
Mechanistic models are dynamic in that quantitative interactions among multiple variables and feedback processes can be explicitly considered. Forecasted changes in disease risk are based on current interactions of physical and biological variables; thus, most process-based models have not considered various kinds of adaptation or evolution in the many factors that determine transmission or host response.
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From page 66... ...
For instance, one impediment to using mechanistic models that mimic complex biological and ecological processes is the reliance on micro-environmental parameters that are seldom available in observational data. · incorporation of experimental data and expert opinion.
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From page 67... ...
One approach that can be used to compensate for a paucity of epidemiological data is to conduct a "meta-analysis" of data pooled from numerous studies. This approach is difficult to apply when there are few studies that cover similar temporal/spatial scales or that focus on similar driving forces and outcomes.
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From page 68... ...
Environmental Protection Agency to develop controls for key waterborne disease agents (U.S. EPA, 1989; Regli et al., 1991; Rose and Gerba, 1991)
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From page 69... ...
. Clinical and surveillance data are used to describe what microorganisms are causing what diseases, and quantitative analyses are undertaken to determine the spectra of disease outcomes.
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From page 70... ...
. In some cases, studies can be designed to relate environmental factors directly to disease or probability of infection, such as the example mentioned earlier where mice were exposed to influenza virus at varying levels of relative humidity to examine how this affected infection rates.
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From page 71... ...
It is an iterative process wherein insights from the scientific and stakeholder communities are communicated to the decision-making community, and, in turn, the evolving informational needs of decision makers provide input for future research. A wide variety of research methods fall under the rubrick of IA.
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From page 72... ...
Since infectious disease transmission dynamics are complex systems that can display spontaneous or socially based adaptive responses, some researchers have attempted to develop algorithms that incorporate a capacity for adaptive change and "learning" to simulate such processes (Janssen and Martens, 1997; Sethi and Jain, 1991~. It is inevitable that some natural and social phenomena will be oversimplified with such approaches; however, integrated models can provide a useful complement to more focused models that provide highly detailed representation of these complex processes.
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From page 73... ...
One of the most critical obstacles to improving our understanding of climate/disease linkages is the lack of high-quality epidemiological data on disease incidence for many locations. These data are needed to establish an empirical basis for assessing climate influences and to develop and validate predictive models.
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From page 74... ...
For the purpose of understanding climate/disease linkages, however, much can still be learned by studying relative changes and patterns of disease transmission. Surveillance programs that capture only a fraction of the actual disease incidence can still provide useful informa
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From page 75... ...
Such extended time series are needed in order to establish a baseline against which one can detect long-term trends or patterns of variability. Remote-Sensing Surveillance Tools In recent years investigators have begun using remotely sensed data collected by satellite-borne instruments to study environmental factors that can influence disease transmission risk for particular regions.
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From page 76... ...
AVHRR has been in continuous operation since 1981, and this multi-decade time series makes it possible to calculate statistically significant variations from the long-term mean value. AVHRR provides data with high enough resolution (1 kilometer)
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From page 77... ...
ANALYTICAL APPROACHES TO STUDYING CLIMATE/DISEASE LINKAGES 77 parameters with health data. For instance, maps of potential vector habitat obtained by analysis of remote-sensing data can be "layered" with geographically referenced data on land use, human population, and so forth, to create maps of disease risk.
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From page 78... ...
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