CLIMATE VARIABILITY IMPACTS ON DENGUE AND VULNERABILITY IN THE CARIBBEAN

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CLIMATE VARIABILITY IMPACTS ON DENGUE AND
VULNERABILITY IN THE CARIBBEAN

• Dharmaratne Amarakoon, Anthony Chen, Roxann
  Stennett
• Climate Studies Group Mona, UWI, Jamaica
• Samuel C. Rawlins, David Chadee
• UWI, St. Augustine Campus Ministry of Health,
  Trinidad

2nd AIACC REGIONAL MEETING, Buenos Aires,
Argentina August 24-27, 2004
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QUESTIONS THAT ARE BEING ANSWERED

• What was the geographical distribution and the
  nature of dengue patterns in the Caribbean?
• What was the nature of the climate variability in
  the Caribbean over the last few decades?
• What are the factors that may impact Dengue
  epidemics, revealed from other studies?
• What were the impacts of climate variability on
  DENGUE seen in the Caribbean?
• What communities are expected to be potentially
  vulnerable and possible reasons for the
  vulnerability?
• How could the results from this impact study be
  utilized to reduce vulnerability?

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DATA METHODOLOGY

• The data acquired for the CCID project by the
  CSGM provided the bulk of the climate data
  Temperature (maximum, minimum and mean) and
  Precipitation, daily or monthly values
• CAREC provided the epidemiology data in the form
  of reported dengue cases and vector indices,
  annual, 4-week period, monthly, quarterly values.
  More attention was focused on reported dengue
  cases
• Data analysis Time series analysis of annual
  reported cases and their rates of change, mean
  temperature, mean precipitation, temperature and
  precipitation anomalies Study of the climatology
  of temperature, precipitation, and reported
  cases Performance of statistical significance
  tests for observed correlations and multiple
  linear regression, wherever applicable.
• ENSO year (El Niño La Niña) classification
  NOAA-CDC MEI index and NCEP/CPC Quarterly SST
  index
• EN 1982/83, 1986/87, 1992/93, 1997/98.
  LN 1988/89, 1998/00
• Supplementary 1994/95
• Main study period 1980 to 2001

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THE CARIBBEAN
Incidence of Dengue
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Figure b
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Figure a
En1
En
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DISTRIBUTION OF EPIDEMICS PEAKS AMONG ENSO PHASES
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Seasonality of the Epidemics and Relation
to Climate Parameters ____________________________
_______________________________ Country
Year Epidemic Peak Temperature
Precipitation
Peak
Peak ________________________________
___________________________ T and T 1995
August (weak) Apr. to Nov Jun. to
Sep. 1996 September
(strong) Apr. to Dec. May to Oct.
1997 December (strong) May to
Dec. July and Nov. 1998
July to Sep. (strong) March to Nov. May to
Sep. 1999 September (weak)
Apr. to Dec. Jul. to Oct. Barbados1995
October (strong) Apr. to Nov. Jul.
to Oct. 1996 September
(weak) Apr. to Nov. May to Nov.
1997 November (strong) Apr. to
Nov. June to Nov.
1998 Aug. to Sep. (weak) Apr. to
Oct. Jul. to Nov.
1999 November (weak) Apr.
to Nov. Jun. to Nov. _______________________
______________________________________
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Recent analysis of Caribbean temperature by
Peterson and Taylor et al (2002) show increasing
trend
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Time Series of Rainfall and Temperature anomalies
at Piarco in
Trinidad
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TIME SERIES ANALYSIS OF TEMPERATURE AND RAINFALL
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IMPACTS SEEN IN OTHER STUDIES

• Hales et al.,(1996)- Association of upsurges of
  dengue in south pacific islands with ENSO events.
• Gagnon et al.,(2001)- Statistically significant
  correlation (gt90 confidence level) between
  dengue epidemics and El Nino events in French
  Guiana, Indonesia, Colombia and Surinam.
• Poveda et al.,(2000)- Association of dengue peaks
  in Colombia during El Nino1 years due to
  temperature increases and stagnant water
  collected for use during drought.
• Campione-Piccardo et al.,(2003)-Monthly reports
  of dengue cases and virus isolates following the
  rainfall with a lag of two to three months, in
  Trinidad and Tobago.
• Focks et al.,(1995)- Possibility of shortening of
  EIP (Extrinsic Incubation Period) at higher
  temperatures.
• Koopman et al.,(1991)-Possibility of higher
  transmission rates of dengue at shorter
  incubation periods.
• Wegbreit (1997)-Statistically significant
  relationship between temperature and dengue
  incidence rates in T T, given a lag of about
  six months.

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CORRELATION RESULTS OF ANNUAL DENGUE CASES WITH
TEMPERATURE AND RAINFALL
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LAG CORRELATION RESULTS (Multiple Regression)
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Wegbreit (1997)
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MonthlyVariability OF Rainfall, MeanT and Breteau
Index in 2003 T T
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RESULTS SUMMARY

• There is a well defined seasonality in the
  epidemics.
• Probability of epidemics during El Nino and El
  Nino1 years is high.
• Both temperature and rainfall influence dengue
  outbreaks. Inter-annual variability is more
  associated with temperature (warming) and
  intra-annual variability is linked more to
  rainfall variability.

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SCENARIOS LEADING TO VULNERABILITY(POTENTIAL
BREEDING PLACES)
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POTENTIALLY VULNERABLE COMMUNITIES

• Having no knowledge of the disease and
  vulnerabilty.
• With poor environmental conditions, including
  sanitation.
• That are densely populated.
• Without suitable water supplies (pipe borne
  water) which results in water collection in
  containers for longer periods of use.

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POSSIBLE REASONS FOR VULNERABILITY

• Lack of resources (funds, manpower).
• Absence of active vector eradication programmes
  (no regular spraying, no use of bacteria like BT
  Bacilus Thuringien).
• Absence of relevant education programmes on
  awareness.
• Absence of procedures to monitor the communities
  and environmental conditions and upkeep.
• Socio-economic status of communities (poverty,
  high population density).
• Insufficient knowledge on vector dynamics and
  virus replication.

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How could the results from this impact study be
utilized to reduce vulnerability ?Develop early
warning systems based on the seasonality, lag and
future climate predictions, leading to effective
programmes on public awareness and education.
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Best Option to reduce Vulerability
Public Awareness Education
CLEAN-UP OR PAY-UP!
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Thank you