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PUBLIC HEALTH AND ECOLOGICAL INTERCONNECTIVITY: A CONDITIONAL PROBABILITY APPROACH ASSOCIATING DEGRADATION OF STREAMS AND INFANT MORTALITY
Citation:
PAUL, J. F., M. E. MCDONALD, AND S. F. HEDTKE. PUBLIC HEALTH AND ECOLOGICAL INTERCONNECTIVITY: A CONDITIONAL PROBABILITY APPROACH ASSOCIATING DEGRADATION OF STREAMS AND INFANT MORTALITY. Presented at Maryland Water Monitoring Council 12th Annual Conference, North Linthicum, MD, November 16, 2006.
Impact/Purpose:
purpose
Description:
Effective public health policy should not be based solely on clinical, individualbased
information, but requires a broad characterization of human health conditions
across large geographic areas. For the most part, the necessary monitoring of human
health to establish these baselines is not being systematically conducted across the
country, and without these baselines, changes in public health through time cannot be
determined. If these baselines existed and changes from these baselines were being
tracked, appropriately designed individual-based studies could more effectively focus on
causation. An example of long-term public health assessments are mortality records,
which have played a prominent role as demographic barometers of community health
since the 19th century. Some morbidity and mortality data are still collected and
national indices are produced. By taking a more historic public health viewpoint that
mortalities and morbidities can be treated as human health endpoints that we would like
to improve, we can explore hypotheses about associations with adverse health
outcomes and reduce the potential number of stressors that must be screened.
Infant mortality rate (IMR, deaths of children up to one year of age / number of
live births over a calendar year) is a public health metric that is routinely collected and is
often used to compare the health and well-being of populations across and within
countries. In the US, premature births, and associated low birth weight, has been the
leading factor associated with most infant deaths, with respiratory distress syndrome
and sudden infant death syndrome also major contributors. Unfortunately, recent data
show an increase in the US IMR for the first time in 40 years, and suggests that we may
not reach our goal of reducing the IMR to 4.511000 by 2010. The high IMR in the US
has been attributed to disparities in IMR among racial and ethnic groups in this country,
particularly African Americans.
Research in the US has focused on various social, biological, and environmental
factors that could be responsible for the elevated level of IMR for specific racial groups.
Obviously, infant health is the net result of a complex interaction of these factors, but
environmental insults may play a major role in that infants are subjected to these both
directly and indirectly through their mother, and have, among other characteristics, a
poorly developed ability to break down and eliminate pollutants for a short period after
birth.
The ecological condition of streams is a robust, widely used measure of water
quality and, therefore, of the environment. The state of Maryland has measured stream
condition using a probability sampling approach consistent with those employed by
USEPA's Environmental Monitoring and Assessment Program. The data are reported
as percent of stream miles within each county in poor ecological condition.
To examine for a relationship between poor water quality and increased IMR. we
used a conditional probability analysis approach with existing, publicly available data.
We examined the null hypothesis that the probability of IMR in a county exceeding the
national norm does not change with the extent of poor water quality, with ecological
condition of streams in the county as the measure. We also examined whether air
quality factors and economic status could account for racial differences in IMR.
Data for the state of Maryland aggregated at the county level for the ecological
condition of streams (1994-1997) and CDC Compressed Mortality File of infant
mortality (1989-1998) were used. A conditional probability relationship between stream
condition and infant mortality was developed in terms of the probability that infant
mortality in a county was higher than the national norm (8.2 per 1000 for 1989-1998)
when a given value for proportion of stream miles within the county with poor ecological
condition was exceeded. We also evaluated the contribution of race, median per capita
income, and data from EPA's Toxic Release Inventory in this relationship. The
robustness of the result for MD was tested by conducting similar analyses for PA and
WV.
Using conditional probability analysis on the readily-available, publicly accessible
data, we found a relationship between the extent of a county's streams in poor
ecological condition and the probability of a county's infant mortality rate exceeding the
national norm. This is the first time that a statistically significant association between
stream condition and infant mortality rate has been shown. This is a relatively robust
relationship as it remains consistent across at least three states in the mid-Atlantic
region of the US. The relationship does not imply that stream condition causes infant
mortality. Rather this relationship means there may be a common stressor for both that
if understood, we could more effectively target our remediation efforts When this
relationship was examined by race, unexpectedly the association held for white infants,
but did not for black infants. We also observed significant associations of white IMR
with TRI data and economic status, in addition to water quality. We observed no
significant association of black IMR with our measure of water quality, median per
capita income, or Toxic Release lnventory data.
Protection of both the environment and public health can be achieved more
effectively and efficiently by understanding the common factors that link the two. As we
improve our understanding of these linkages, environmental and public health officials
may develop greater insights into possible underlying causative factors and, therefore,
preventative measures. This will require greater coordination and cooperation among
practitioners in all fields.