Environment, Society, Climate and Health: Analysis, Understanding and Prediction

Mark L. Wilson Department of Epidemiology and Global Health Program School of Public Health The University of Michigan Summer Colloquium on Climate and Health NCAR Boulder, Colorado 19 July, 2006

Outline

1. Introduction: Infectious Disease Epidemiology 2. Patterns of Environmental Influences 3. Climate as an Environmental Driver 4. Discussion of examples from your research/interests 5. Examples from my lab's research (IF TIME PERMITS)

Classical Epidemiological Triad

Environment Agent Host

Environment* (biophysical, psycho-social, etc.) Agent (diverse exposures, including non contagious ) Host (animal, plant, ultimately human) *CLIMATE is an Environmental Influence

Agent Environment Host

Examples Involving Infectious Diseases Environment longevity & infectivity outside host host distribution, abundance, infection

e.g. cholera hantaviral disease hookworm schistosomiasis

nutrition hygiene treatment housing

e.g. TB, HIV/AIDS, diarrheal diseases, acute respiratory infections

Agent tissue tropisms, pathogenicity, immune response, host specificity

e.g. rabies, Lyme disease, malaria, cryptosporidiosi.

Host

But for ALL diseases, complex interactions occur...

Environment Altered hygiene Improved irrigation Redesigned housing Better nutrition Agent Host

Environment Agent transport to new areas New antibiotics, pesticides Labor actions affecting toxin exposure Agent Host

Environment Agent Host Exposure probability, host immunity, support networks, availability of supportive care

Examples of Environmental and Epidemiological Data

• • • • • • •

Climate patterns – variability… perhaps change… Land Use / Land Cover patterns Human case data (specific or syndromic) Vector abundance and pathogen infection Reservoir abundance / infection prevalence Environmental use and exposures Economic development, human demography, migration … more

Each of these is historically changing in time and space

Environmental Determinants of Human Disease

Social and Economic Policies Institutions (including medical care) Living Conditions Social Relationships Individual Risk Factors Genetic/Constitutional Factors Pathophysiologic pathways Individual/Population Health

Modified from Kaplan, 2002

Research Challenge – Analyze and understand interactions!

Social and Economic Policies Institutions (including medical care) Living Conditions Social Relationships Individual Risk Factors Genetic/Constitutional Factors Pathophysiologic pathways Individual/Population Health

Climate Variability vs. Climate Change

• Climate

Change

:

- persistent change or trend in mean atmospheric conditions - current changes unprecedented in human history

• Climate

Variability

:

- day-to-day (weather) or relatively short term (seasonal) changes in atmospheric conditions - effects on disease patterns most easily analyzed, and used in forecasts

What is climate change? Climate variability?

Unchanging Average, Unchanging Extremes

Average Trend (solid line) Actual Measure (dashed line)

Low Time

Low Unchanging Average, Increasing Extremes

Average Trend (solid line) Actual Measure (dashed line)

Time

Low Increasing Average, Unchanging Extremes

Average Trend (solid line) Actual Measure (dashed line)

Time

High Different Rates of Increasing Averages

Average Trend (solid line) Actual Measure (dashed line)

Low Time

Low Increasing Average, Greater Extremes

Average Trend (solid line) Actual Measure (dashed line)

Time

Low Increasing Rate of Increasing Average, Unchanging Extremes

Average Trend (solid line) Actual Measure (dashed line)

Time

Low Increasing Rate of Increasing Average, Greater Extremes

Average Trend (solid line) Actual Measure (dashed line)

Time

Each of these climate change patterns may have different impacts on particular disease risks.

Effects will depend on the ecology of transmission and the etiology and expression of disease.

Each exposure type should be considered in context of:



PERSON (age, behavior, gender, SES, etc.)

 

TIME (year, season, adjacent periods, etc.) PLACE (geographic location, habitat, proximity, etc.) Most Epidemiological studies only superficially consider this for environmental (climatic) exposures:

+

PERSON most often involves standard descriptors that do not include "social" characteristics or other environmental exposures (e.g. climatic).



TIME is rarely dynamic, considers only recent past, and climate pattern over long periods not always available.



PLACE often ignored or not carefully evaluated (e.g. spatial autocorrelation, climate patterns in regions may be important ) .

Direct Exposure

Source Humans Humans Humans Animals Animals Humans

Environment and Exposure

Indirect Exposure Environmental Exposures

Source Vehicle

Solar UV EM Radiation Tetanus

Anthroponotic Infections

Humans

Stream pollutants Air Particulates Legionella

Humans Vehicle

STDs Measles Hepatitis B

Zoonotic Infections

Humans Vehicle

Malaria Dengue Roundworm

Animals Vehicle Vehicle Animals Humans

Anthrax Ebola (?) CJD Lyme Disease Hantaviral Disease Most arboviral diseases

Direct Exposure

Environment and Exposure Where might Climate Impact?

Indirect Exposure

Source

Environmental Exposures

Vehicle Humans

Solar UV EM Radiation Tetanus

Source Humans

Stream pollutants Air Particulates Legionella

Direct Exposure

Environment and Exposure Where might Climate Impact?

Indirect Exposure

Humans Humans

Anthroponotic Infections

Vehicle

STDs Measles Hepatitis B

Humans Humans Vehicle

Malaria Dengue Roundworm

Direct Exposure

Environment and Exposure Where might Climate Impact?

Indirect Exposure

Animals Animals Humans

Anthrax Ebola (?) CJD

Zoonotic Infections

Vehicle Humans Animals Animals Vehicle

Lyme Disease Hantaviral Disease Most arboviral diseases

What diseases are the most climate sensitive?

High Low

– heat stress – effects of storms – air pollution effects – asthma – vector-borne diseases – water-borne diseases – food-borne diseases – sexually-transmitted diseases

Respiratory Diseases

• Climate change impacts on air pollutants uncertain –

ozone increase most likely

• Longer growing seasons, long-term climate changes may alter pollen burden • May alter incidence or exacerbate –

may affect development of asthma

• Adaptive Measures – –

early warning systems public education

Water-borne Diseases

• Cholera

– growing understanding of climate linkage – controlled by sanitary infrastructure in developed countries

• Cryptosporidiosis

– linkage to extreme precipitation events – may lead to higher pre-treatment levels

Vector-Borne Diseases

• Many parts of complex systems are climate sensitive – –

vector survival, reproduction, development, biting rates pathogen reproduction, development

• Disease activity depends on multiple factors and is region specific –

lifestyle, vector control measures, medical care

• Vigilance needed for imported diseases –

Greatest threat remains foreign travel, border areas

Sexually transmitted Disease Heat stress Effects of Storms Water-borne disease Food-borne disease Atherosclerosis Asthma Cancer (not skin) Vector-borne Disease Myocardial Infarction Violence

More Climate Sensitive

Committee Members

DONALD BURKE (Chair) Johns Hopkins University ANN CARMICHAEL DANA FOCKS Indiana University U.S. Department of Agriculture DARELL GRIMES JOHN HARTE SUBHASH LELE PIM MARTENS University of Southern Mississippi University of California, Berkeley University of Alberta Maastricht University, Netherlands JOHNATHAN MAYER LINDA MEARNS ROGER PULWARTY LESLIE REAL CHET ROPELEWSKI JOAN ROSE ROBERT SHOPE JOANNE SIMPSON MARK WILSON University of Washington National Center for Atmospheric Res. University of Colorado / NOAA Emory University Intl. Research Inst. for Climate Prediction University of South Florida University of Texas Medical Branch NASA Goddard Space Flight Center University of Michigan LAURIE GELLER SUSAN ROBERTS JONATHAN DAVIS

NRC Staff

Board on Atm. Sciences and Climate Ocean Studies Board Institute of Medicine

KEY FINDINGS 1:

Climate-Disease Linkages

Weather fluctuations and seasonal-to interannual climate variability influence many infectious diseases

•

Characteristic geographic distributions and seasonal variations of many infectious diseases (IDs) are prima facie evidence of linkages with weather and climate.

•

Studies have shown that temperature, precipitation, humidity affect life cycles of many pathogens, vectors (directly and indirectly); this, in turn, may influence timing, intensity of outbreaks.

•

However, ID incidence also affected by other factors (e.g. sanitation, public health services, population density, land use changes, travel patterns.

•

The importance of climate relative to these and other variables must be evaluated in the context of each situation .

KEY FINDINGS 2:

Climate-Disease Linkages

Observational and modeling studies must be interpreted cautiously

•

Numerous studies showing associations between climatic variations and ID incidence can not fully account for complex web of causation underling disease dynamics; most are not reliable indicators of future changes.

•

Various models simulating effects of climatic changes on incidence of diseases (e.g. malaria, dengue, cholera) are useful heuristic tools for testing hypotheses and undertaking sensitivity analyses; they are not intended to serve as predictive tools; often exclude physical/biological feedbacks and human adaptation.

•

Caution needed in using these models to create scenarios of future disease incidence, providing early warnings, and developing policy decisions.

KEY FINDINGS 3:

Climate-Disease Linkages

The potential disease impacts of global climate change remain highly uncertain

•

Changes in regional climate patterns caused by long-term global warming could affect potential geographic range of many diseases.

•

However, if climate of some regions becomes more suitable for transmission of particular disease agents, human behavioral adaptations and public health interventions could serve to mitigate many adverse impacts.

•

Basic public health protections (adequate housing, sanitation),and new interventions (vaccines, drugs), may limit future distribution & impact of some infectious diseases, regardless of climate-associated changes.

•

These protections, however, depend on maintaining strong public health programs, and assuring vaccine and drug access in poorer countries.

KEY FINDINGS 4:

Climate-Disease Linkages

Climate change may affect the evolution and emergence of infectious diseases

•

Potential impacts of climate change on the evolution and emergence of infectious disease agents are an additional highly uncertain risk.

•

Ecosystem instabilities from climate change and concurrent stresses (e.g. land use changes, species dislocation, increasing global travel) could influence genetics of pathogenic microbes through mutation and horizontal gene transfer.

•

New interactions among hosts and disease agents could occur, fostering emergence of new infectious disease threats.

KEY FINDINGS 5:

Climate-Disease Linkages

Potential pitfalls exist in extrapolating climate and disease relationships among spatial and temporal scales

• • • • •

Relationships between climate and infectious disease are often highly dependent upon local-scale parameters .

Difficult or impossible to extrapolate broader spatial scales. these relationships meaningfully to Temporal climate variability (seasonal, interannual) may not represent a useful analog for long-term impacts of climate change.

Ecological responses on such time scales (e.g. El Niño event) may be significantly different from the ecological responses and social adaptations expected under long-term climate change. Long-term climate change may influence regional climate variability patterns, hence limiting the predictive power of current observations.

climate

mean temperature, precipitation, humidity, extreme weather events

ecology

vegetation, soil moisture, species competition

transmission biology

microbe replication/movement, vector reproduction/movement, microbe/vector evolution

disease outcome

Risk, rate of transmission Spread to new areas

social factors

sanitation, vector control, travel/migration, behavior/economy, population/demographics

KEY FINDINGS 6:

Climate-Disease Linkages

Recent technological advances should improve modeling of infectious disease epidemiology

•

New techniques in several disparate scientific disciplines may encourage different approaches to infectious disease models.

•

Advances include sequencing of microbial genes, satellite-based remote sensing of ecological conditions, Geographic Information System (GIS), new analytical techniques, increased computational power.

•

Such technologies should improve analyses of microbe evolution and distribution, and of relationships to different ecological niches.

•

This may dramatically improve abilities to quantify disease impacts from climatic and ecological changes.

KEY FINDINGS 7:

Disease "Early Warning" Potential

Future epidemic control strategies should complement "surveillance and response" with "prediction and prevention"

• • • •

Current epidemic control strategies depend largely on surveillance for new outbreaks followed by a rapid response to control the epidemic . Climate forecasts and environmental observations could help identify areas at risk of epidemics , thus aiding efforts to limit or prevent.

Operational disease early warning systems not yet feasible due to limited understanding of climate/disease relationships and climate forecasting. Establishing goal of developing early warning capacity will foster the needed analytical, observational, and computational developments.

KEY FINDINGS 8:

Disease "Early Warning" Potential

Effectiveness of early warning systems will depend upon context of their use

•

Where risk mitigation is simple and low-cost , early warning may be feasible given only general understanding of climate/disease associations.

•

If mitigation actions are significant, precise and accurate prediction may be necessary , requiring more thorough mechanistic understanding of underlying climate/disease relationships.

•

Value of climate forecasts depends on disease agent and locale (e.g. reliable ENSO-related disease warnings restricted to regions with clear, consistent ENSO-related climate anomalies).

•

Investment in sophisticated warning systems not effective use of resources where capacity for meaningful response is lacking, or if population not highly vulnerable to hazards being forecasted.

KEY FINDINGS 9:

Disease "Early Warning" Potential

Disease early warning systems cannot be based solely on climate forecasts

•

Need for other appropriate indicators (e.g. meteorological, ecological, epidemiological surveillance) that complement climate forecasts.

•

Such combined information may permit a “

watch

” to be issued for regions, and a “

warning

” if surveillance data confirms projections.

•

Vulnerability and risk analyses, feasible response plans, and strategies for effective public communication needed as part of system.

•

Climate-based early warning for other applications (e.g. agricultural planning, famine prevention) may provide many useful lessons.

KEY FINDINGS 10:

Disease "Early Warning" Potential

Development of early warning systems should involve active participation of the system’s end users

•

Input from stakeholders (e.g. public health officials, local policymakers) needed to help ensure that forecast information is provided in a useful manner and that effective response measures are developed.

•

Probabilistic nature of climate forecasts must be clearly explained to communities using these forecasts, allowing development of response plans with realistic expectations of possible outcomes range.

RESEARCH RECOMMENDATIONS

•

Research on climate and infectious disease linkages must be strengthened

•

Further development of transmission models needed to assess risks posed by climatic and ecological changes

•

Epidemiological surveillance programs should be strengthened

•

Observational, experimental, and modeling activities must be coordinated

•

Research on climate and infectious disease linkages inherently requires interdisciplinary collaboration

Unpredictability of climate-disease linkages suggests reducing human vulnerability is most prudent public health strategy

•

Understanding of climate linkages to ecosystems and health not yet solid, making most early warning systems not yet feasible.

•

Some unpredictability will always be present.

•

Thus, strengthening of public health infrastructure (e.g. vector control, water treatment systems, vaccination programs) should be high priority.

•

Reducing overall vulnerability of populations at risk is the most prudent strategy for improving health.

Knowing is not enough; we must apply.

Willing is not enough; we must do.

(Goethe)

• • • • • • •

Discussion… From YOUR EXPERIENCES or INTERESTS: What diseases might have a climate link and what climate variables might impact on which diseases?

WHY? What are the biological or social pathways?

How would these be investigated/researched?

What additional information would you seek?

How would you integrate this into OTHER determinants of risk?

Could you forecast risk based on these analyses alone?

What other factors should be considered and why?