date collection efforts so that differences across time and among subpopulations can be effectively monitored. For others, although there is evidence that they influence health, the challenge is to develop more adequate means of understanding the nature of their influences. In these instances, attention needs to be focused on using data collection to facilitate studies of the way in which they operation on populations and subpopulations.

OVERVIEW

The measurement of many influences poses methodological challenges that must be considered and systematically addressed in future research, surveys and evaluation studies. For such factors as biological influences on children’s health, invasive medical tests may be necessary and raise potential ethical questions about risk-benefit ratios of specific assessment procedures. In other cases, the need for highly personal information raises confidentiality concerns and concerns about unintended consequences of shared information. In still other instances, such measures as policy influences may require aggregation across governmental units and agencies.

Several overall issues must be considered to improve the measurement of influences on children’s health. First, how do various influences interact with one another over time to affect health? Specific influences may set in motion a chain reaction, unleashing other biological and behavioral processes than can cascade toward a specific outcome (final common pathway) or a range of potential outcomes (multiple pathways). Since each interaction in such a cascade is potentially a point to monitor and intervene, understanding and measuring such effects become important methodological challenges. As a specific developmental stage or sensitive period, exposure to a specific influence can unleash a cascade of effects with significant short- and long-term impacts, whereas the same exposure at a different stage may have a muted or minimal effect.

Another challenge is how to understand and model the effect of multiple influences for policy purposes. For example, when a child is exposed to multiple adverse influences at the biological, behavioral, family, and community levels, are these factors simply additive, or are they multiplicative (Rutter, 1994; Werner, 1993)? The most effective prevention and intervention strategies may target high-risk groups (i.e., those affected by multiple risk factors), rather than using strategies that address single risk factors. For policy purposes, which children may be most at risk for later adverse outcomes, and which may be most in need of special assistance?

Aggregation of data on influences at the individual, family, and community levels is complicated (Small and Supple, 2001) and prone to errors in the application of statistical techniques, drawing appropriate causal inferences, and estimating the relative size of influences’ effects.

Apart from biomarkers, the physical environment, family demography, and



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