environment, society, climate and health: analysis, understanding and prediction part 2 mark l....

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

PART 2

Mark L. WilsonDepartment of Epidemiology

andGlobal Health ProgramSchool of Public Health

The University of Michigan

Colloquium on Climate and HealthNCAR

Boulder, Colorado23 July, 2004

Some examples of studies from our group

• Importance of environment (BROADLY defined)

• Role of spatial pattern of people, people-environment

- spatial autocorrelation as a problem

- spatial pattern as a source of insight

• Attempt to integrate individual- and population-level

• Increasing use of time series, time-space analyses

• Longer-term: integrate these analyses and underlying methods with more "upstream" causes

• Summarize NAS report findings

Spatially-Extensive Examples

•Large scale active surveillance

•Remote sensing of environment

•Population Census – human / animal

Cutaneous Leishmaniasis - Turkey

(Collaboration with Aksoy et al.)

(Collaboration with Aksoy et al.)

Dengue Fever and Water Sources - Peru

Schneider et al. 2004 in press (collaboration with Morrison, et al.)

N

1000 0 1000 2000 Meters

Average Wing Length by City Block

ZonesBGIQMCMYPTPUSATA

Average Wing Length1.8667 - 2.40452.4045 - 2.5752.575 - 2.64962.6496 - 2.82.8 - 3.1167

Schneider et al. 2004 in press (collaboration with Morrison, et al.)

Spatial Patterns of Malaria Risk - Kenya

Macdonald et al. in prep (collaboration with Hawley, Hightower, et al.)

Hot spot and cold spot clusters for

Anopheles gambiae


Some Conclusions • Clusters of apparently higher risk • No obvious link to mosquito breeding sites• Associations with crude measures of SES weak• Pattern of higher risk suggests possible role of

regional environmental factors• Generates new hypotheses for more focused

studies

Other Malaria Studies• Malawi - analysis of role of ITNs in reducing childhood

anemia and mortality (Don Mathanga)- measures SES, knowledge, access and use- ITNs highly effective, also efficacious- ORs for income, educ., housing all signif.

• Kenya - urban malaria and patterns of environmental and SES inequality (Jose Siri)

- cases/controls, questionnaire KAP, household environment, RS and ground-based environmental data

- strong spatial clustering of cases, environment vars.- KAP and SES data being analyzed

Temporally-Extensive Data

- Examples

•Systematic surveillance of cases

•Long-term samples of environment

•Population Census – human / animal

1993

0

50

100

150

200

250

Nu

mb

er o

f C

ases

1994 1995 1996 1997 1998 1999 2000 2001

Year

Viral Meningitis in Michigan

• Collaboration with State Epidemiologists• County-specific case data from 1993-2001• Cases adjusted to county population & area

Viral Meningitis - Michigan, 1993-2001

• Time series of cases

• Autocorrelation function

ACF=0.43 at 3-year lag

Figure 5. The Three Most Likely Overall Spatio-Temporal Clusters

42 counties, July - Oct., 2001

5 counties + Detroit, July - Oct., 1998

2 counties, Aug - Oct., 2001

The three clusters were each significant, with p-value = 0.01

In the most likely cluster (#1), children <10 years old were 42% of all cases, while for all 8,803 cases, this age group constituted 34%. X2 test for specified proportions: X2=36.5, d.f.=1, p-value <0.0001

2

3

1

Viral Meningitis in Michigan

Greene et al. In press

Influenza and Environment• Investigation of relationships among epidemic

onset, duration, magnitude, predominant circulating strain(s) - and climate signals

• Are climate-influenza relationships region-specific?

• Are relationships consistent across years?

Potential Mechanisms

• Climate could influence:

– onset of transmission

– cessation of transmission

– patterns of contagion

– apparent inter-epidemic virus disappearance

– regional synchrony of transmission

– virus extra-host survival

– human immunity

– disease expression

– human-to-human contact patterns

– non-human host abundance and behavior

Specific Potential Mechanisms

• Temperature– virus survival– defense mechanisms of URT– crowding

• Humidity– Assays show infectivity of influenza virus declined rapidly

under conditions of 40% humidity1

– Conditions of low indoor humidity during winter could promote virus survival and ↑ transmission

1Saito et al. (2003) Options for the Control of Influenza V, poster.

Environmental Data: Multivariate El Niño Southern Oscillation Index (MEI)

• Sea-level pressure

• Zonal and meridional components of the surface wind

• Sea surface temperature

• Surface air temperature

• Total cloudiness fraction of the sky

•MEI and ENSO temporal patterns similar http://www.cdc.noaa.gov/~kew/MEI/mei.html

Influenza Data from France

0

250

500

750

1000

1250

1500

1750

2000

nov-84 nov-86 nov-88 nov-90 nov-92 nov-94 nov-96 nov-98

CALENDAR DATE

ILI /

100

,000

Surveillance: Influenza-Like-Illness (ILI) all France (500 physicians, 88 provinces) 1984 - present

Viboud, et al. (2004) European Journal of Epidemiology (in press)

Temporal pattern of ILI epidemic magnitude (red) categorized as below or above average (1984-2000) and annual excess mortality (black) (1984-1997). Symbol size is proportional to the value it represents. Monthly Multivariate ENSO Index (MEI) shown as blue curve, left y-axis. Influenza virus variants predominantly circulating in France are indicated for each winter.

MEI

DominantStrain

Influenza - Climate Variability Temporal Pattern


Summary of Effect in France


0

1

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4

5

WARM COLD

ENSO CONDITIONS

ILLNESS(MILLIONS)

0

500

1000

1500

2000

2500

WARM COLD

ENSO CONDITIONS

EXCESS DEATHS

1979-2000: Influenza-related morbidity and mortality greater during cold ENSO conditions

Observed Flu Seasonality - U.S.

• NCHS pneumonia and influenza / respiratory and circulatory mortality

– Age, race, sex, county, metropolitan statistical area, underlying cause, and up to 8 axis conditions listed, with monthly resolution

• National Hospital Discharge Database• Circulating Strains/Vaccine/Match, 1978 – 2003

• For all regions, influenza negatively correlated with MEI • (consistent with Viboud et al. 2004)

– Effect varies by region, with weakest correlation in New England, strongest in Pacific, and intermediate values in between

– Regional trend interesting; repeat using properly grouped seasons from NCHS data and examining lagged climatic drivers

• Viral pneumonia also neg. correlated w/ MEI, but E-W trend not seen

• MEI not correlated with a chronic respiratory disease, asthmaGreene et al. In preparation

Preliminary Analysis in USA

Raccoon Rabies Expansion - Connecticut

• All cases of raccoon rabies, 1991-1996

• Georeferenced to location where found

• Spatio-temporal analysis of spread

• Trend surface velocity analysis for vectors indicating direction and rate of spread

• Simulation modeling of importance of rivers

Time-Space Extensive Data - Examples

First case in each township indicated by darker color


Best fit trend surface vector field showing direction and velocity of spread of the infection


Lucey et al. 2002

Influenza Time-Space Spread - France

Week 1

Week 3

Week 5

Week 7

Summary• Disease and environmental patterns typically vary

temporally, spatially, spatio-temporally

• Environmental factors affect most diseases, but especially so for infectious diseases

• Analysis of space and time patterns can help clarify confounding, identify new associations, develop new hypotheses… and determine lack of independence

• Challenge: better integrate these analyses and underlying methods with studies of more "upstream" causes

What are the health implications for these unprecedented climatic

events?

Committee MembersDONALD BURKE (Chair) Johns Hopkins UniversityANN CARMICHAEL Indiana UniversityDANA FOCKS U.S. Department of AgricultureDARELL GRIMES University of Southern MississippiJOHN HARTE University of California, BerkeleySUBHASH LELE University of AlbertaPIM MARTENS Maastricht University, NetherlandsJOHNATHAN MAYER University of WashingtonLINDA MEARNS National Center for Atmospheric Res.ROGER PULWARTY University of Colorado / NOAALESLIE REAL Emory UniversityCHET ROPELEWSKI Intl. Research Inst. for Climate PredictionJOAN ROSE University of South FloridaROBERT SHOPE University of Texas Medical BranchJOANNE SIMPSON NASA Goddard Space Flight CenterMARK WILSON University of Michigan

NRC StaffLAURIE GELLER Board on Atm. Sciences and ClimateSUSAN ROBERTS Ocean Studies BoardJONATHAN DAVIS Institute of Medicine

Under the Weather: Climate, Ecosystems and Infectious Disease

http://www.nap.edu/books/0309072786/html/

1) provide a critical review of the linkages between climate variability and emergence/transmission of infectious disease agents, and to explore feasibility of using this information to develop a fuller understanding of the possible impacts of long-term climate change.

2) develop an agenda for future research activities that could further clarify these linkages.

3) examine the potential for establishing disease early-warning systems based on climate forecasts and for developing effective societal responses to such warnings.

Sponsors: USGCRP, CDC, NOAA, NASA, NSF, EPA, DOI, EPRI

NAS Committee Tasks

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.


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.

2050 projection (from Martens et al., 1999)

2050 projection (from Rogers and Randolph, 2000)


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.


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.

Direct Transmission

Humans

ANTHROPONOSES

ZOONOSES

Indirect Transmission

Vector

Humans

Humans

Humans

Vector

Animals

Animals

Humans Humans

Vector Vector

Animals

Animals


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 these relationships meaningfully to broader spatial scales.

• 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.

climatemean temperature,

precipitation, humidity,extreme weather events

ecologyvegetation, soil moisture,

species competition

social factorssanitation, vector control,

travel/migration,behavior/economy,

population/demographics

transmission biologymicrobe replication/movement,vector reproduction/movement,

microbe/vector evolution

disease outcomeRisk, rate of transmission

Spread to new areas


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.


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.


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.

Cer

tain

ty

Time

prediction surveillance

environmentalobservations

early cases

sentinel animals

epidemic

climateforecasts

publiccommunication

evaluation,feedback

climateforecasts

ongoing epidemiological surveillance and environmental observations

disease watch/warning

responsestrategy

risk analysis,vulnerabilityassessment


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 solid, making 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.

Thank you…. Questions?

environment, society, climate and health: analysis, understanding and prediction part 2 mark l....

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