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13 May 2003 Exploring Connections Between Ecological Condition and Human Health: County-Level Aggregation for Maryland John F. Paul National Health and Environmental Effects Research Laboratory U.S. Environmental Protection Agency Research Triangle Park, North Carolina 27711

Acknowledgements Steve Hedtke Michael McDonald Wayne Munns Hillel Koren Jim Heltshe Allan Marcus

Context Definitions Working Null Hypothesis Methods and datasets Results Next Steps Presentation Outline

Context Public policy continues to evolve in response to new and complex social, economic, & environmental drivers. Science needs to anticipate, understand, assess, and reduce risks to human health and our environment to support mission of Agency (protect human health and safeguard the environment)

Context (cont’d) Holistic approach to environmental problem solving Future environmental problem solving requires new and creative approaches - out-of-the-box thinking and risk taking

Long-Term Perspective Provide the Agency with the information, methods & models needed to make informed selections among policy options, identify emerging risks, & minimize unintended consequences

Hypothetical Example: Unintended Consequence of Improved Ecological Conditions on Human Health Outcomes Driver Environmental Issue Unintended Consequence Policy environmental protection surface water quality increased riparian buffers Increased risk of Lyme disease & exposure to fecal pathogens

Definition: Eco-Human Connectivity relationship between ecological systems and human health more than exposure-response link condition of ecological systems with human health not necessarily cause-effect

Starting Point View humans and ecological systems as one overall interacting system Some actions can affect both human health and condition of ecological systems Web of causation exists

Race Land Use Social Conditions Transportation Agricultural Practices Population Economy Housing Income InfantMortality StreamCondition Web of Causation

Driver Stressor(s) Infant Mortality Benthic Impairment Health Effect X q Ecological Effect X 2 Indicator D Indicator C Indicator B Indicator A

Background for Working Null Hypothesis Independence: two things are independent if the probability of either is the same whether or not the other occurs If there are actions that can affect both human health and condition of ecological systems, then the appropriate indicators for human health and condition of ecological systems are not independent

Background for Working Hypothesis (cont’d) Conditional probability: probability of something occurring when it is known that something else has occurred Quantitative measure of eco-human connectivity is an appropriately determined conditional probability For example, probability of occurrence of human health outcome for specified condition of ecological system Or more specifically, probability of higher than normal infant mortality for a county if more than half of streams in the county are impaired

Working Null Hypothesis P ( Y ) = probability of Y ( unconditional probability ) P ( Y | X ) = probability of Y if X occurs ( conditional probability ) Eco-human connectivity null hypothesis: P ( Y | X ) = P ( Y ) or conditional probability equals unconditional probability

Working Null Hypothesis (cont’d) If eco-human connectivity exists, then first step is to disprove the null hypothesis Remainder of presentation is focused on this

Data Bases Used for Analyses Maryland Biological Stream Survey Maryland Biological Stream Survey ( )  Lattice sampling of 17 major drainage basins over three-years with probability design  Approximately 300 stream segments sampled each year  Use data for benthic IBI from Compressed Mortality Data Compressed Mortality Data - CDC Wonder ( )  Under 1 year of age  All races  Both genders  All causes of death NOAA Coastal Assessment and Data Synthesis System NOAA Coastal Assessment and Data Synthesis System (1990)  Census of agriculture  Fertilizer use and sales  Pesticides  Land Use /Land Cover  Socioeconomics

Why Select Infant Mortality and Stream Condition? Infant mortality has long been considered a sensitive indicator of the impact of socioeconomic disparities on the health of populations (Gortmaker & Wise 1997) Condition of bottom-dwelling communities in streams is integrative of environmental impacts occurring over recent time

Why Maryland? Only state that has reported survey data for stream condition summarized at county level

Infant Mortality Stream Condition Income Transportation Socioeconomic Conditions Agricultural Practices Population Economy Housing Land Use Race

Infant Mortality (Y) Stream Condition (X) Income Transportation Socioeconomic Conditions Agricultural Practices Population Economy Housing Land Use Race P ( Y | X > Xo)

Application of Conditional Probability Approach Ability to identify undesired human health outcome level exists, e.g., infant mortality greater than 10 per 1000 Treat county as fundamental unit – equal weighting Critical concept 50% of stream miles impaired in countymeans probability of observing impaired streams in county is 50% Preliminaries

Conditional Probability Approach Given – Y – undesired human health outcome - binary (infant mortality in county above 10 per 1000) X – ecological condition – continuous (percent of stream miles in county with impaired benthos) X o – conditional value for ecological condition Two-step approach to calculate P ( Y | X > X o ) – 1. Identify subset of population with X > X o 2. Determine fraction of subset with undesired outcome Calculate P ( Y | X > X o ) for all observed values of X

The basic underlying question: Is the association real or fantasy? Answer by trying to disprove null hypothesis

Test null hypothesis for individual stream condition value Can’t use t-test, two sets tested are not independent Result using bootstrap estimation: If greater than 78% of stream miles in county are impaired there is less than 2% chance that value for probability of infant mortality exceeding 10/1000 could have occurred randomly Look at entire range of values to determine when null hypothesis disproved – Bayesian model to account for uncertainty

Probability of infant mortality > 10/1000 Bayesian Model (mean and 95% posterior intervals)

Md Pa WVa Md: MBSS Pa, WVA: EMAP-SW

Risk calculations for infant mortality in a county for fraction of stream miles in county with impaired benthos (number of counties in each category)

Where is this all going? Continue evaluation of eco-human connectivity for additional data sets Interesting observations can lead to hypothesis generation Useful for identification of gaps/needs/unidentified problems Web of causation - Possible tool to help target management actions Web of causation - Conceptual model for problem identification/formulation in risk assessment Limitation: County is smallest level of aggregation for mortality data

Unhealthy Streams – Unhealthy Children Unhealthy Ecosystems – Unhealthy People Healthy Ecosystems – Healthy People

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