Epidemiology Patterns Of Dengue In The Caribbean

Under Climate ChangeA. M. D. Amarakoon**, Anthony A. Chen,

Michael A. Taylor, Rainaldo F. CrosbourneClimate Studies Group Mona, UWI, Jamaica

Samuel C. Rawlins, Karen Polson

Caribbean Epidemiology Centre, Trinidad & Tobago

Wilma Bailey, Charmaine Thomas-Heslop

Department of Geography, UWI, Jamaica

[** SPEAKER]

The Threat of Dengue Fever - Assessment of Impacts and

Adaptation to Climate Change in Human Health in the Caribbean

An AIACC Project at The University of the West Indies,

Mona and Caribbean Epidemiology Centre

PROJECT: AIACC-SIS06

THE CARIBBEAN

OBJECTIVES To determine the extent of the association

between climate and the incidence of dengue across the Caribbean Region.

To explore possible adaptation options

The approaches selected to achieve the objectives:

Investigate the influence of climate, through temperature and precipitation, on the epidemics

Investigate the seasonality (seasonal variability of the epidemic)

Investigate the degree of association of dengue epidemics with ENSO events

Examine, briefly, some adaptation options.

Previous studies/events that influenced the selected approaches:

• Hales et al (1996), Poveda et al (2000), Gagnon et al (2001)

• Koopman et al (1991), Focks et al (1995)

• Ropelewski and Halpart (1996), Chen et al (1997), Malmgren et al (1998), Taylor (1999), Chen and Taylor (2001)

• AIACC V & A workshop, Trieste, Italy, June 2002

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 (Fisher’s exact test using suitable contingency tables) for observed correlations, wherever applicable.

• ENSO year (El Niño & La Niña) classification: NOAA-CDC MEI index {EN: 1982/83, 1986/87, 1992/93, 1997/98. LN: 1988/89, 1998+/00} Supplementary: 1994/95 • Main study period: 1980 to 2001

El Niño La NiñaMEI

Caribbean- Reported Cases

-6000

-4000

-2000

0

2000

4000

6000

8000

10000

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Annual totals

Rate of change

CAREC 4-WEEK ACCUMULATION (1995-2001)

0

1000

2000

3000

4000

5000

6000

1 2 3 4 5 6 7 8 9 10 11 12 13

4- Week period

Acc

um

ula

ted

rep

ort

ed c

ases

Average 4-weekperiod accumulation

Jn D

En+1En

T & T Annual Reported Cases and Rate of Increase

-3000

-2000

-1000

0

1000

2000

3000

4000

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Year

Annual Totals

Rate of Increase

Some Case Studies: T & T

Temp Anomalies (Piarco)

-2.5

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

19

80

19

81

19

82

19

83

19

84

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

Temp Anomalies

Time Series of Temperature Anomalies: 1980 to 2001

Rainfall Anomalies (Piarco)

-2

-1.5

-1

-0.5

0

0.5

1

1.5

2

2.5

3

19

70

19

72

19

74

19

76

19

78

19

80

19

82

19

84

19

86

19

88

19

90

19

92

19

94

19

96

19

98

20

00

Rainfall Anomalies

Time Series of Rainfall Anomalies: 1970 to 2001

-100

100

300

500

700

900

1100

4-Week period reported cases in T & T: 1995 to 1999

4-Week period rainfall: 1995 to 1999

Time Series of Reported Cases & Rainfall (mm) in 4-Week Periods

95Jan 96Jan 97Jan 98Jan 99Jan 99Dec

MONTHLY VARIATION OF MEAN T (T & T: 1995-1999)

25.5

26

26.5

27

27.5

28

28.5

29

29.5

0 2 4 6 8 10 12 14MONTH

ME

AN

T in

oC

1995

1996

1997

1998

1999

Average House Index: 1996-2001

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Average Index: 1996-2001

A sample of Monthly Variability in House Index: Port of Spain City Co-operation

Some Results For JamaicaMONTHLY VARIATION OF RAINFALL (JAMAICA: 1993,

1995, 1997 & 1998)

-50

0

50

100

150

200

250

300

350

0 2 4 6 8 10 12 14

MONTH

RA

INF

AL

L i

n m

m

1997

1998

1995

1993

4-WEEK VARIATION OF CASES (JAMAICA: 1995, 1997, 1998 and 2001)

-100

0

100

200

300

400

500

600

700

800

900

0 5 10 15 20

4-WEEK PERIODS

RE

PO

RT

ED

CA

SE

S

1995: cases 1778

1997: cases 17

1998: cases 1255

2001: cases 39

Monthly Mean Temperature in C

24

25

26

27

28

29

30

31

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

1993

1995

1997

1998

Reported Cases

Jn D

-2000

-1500

-1000

-500

0

500

1000

1500

2000

1980 1982 1984 1986 1988 1990 1992 1994 1996 1998 2000 2002

Totals

Rate of incearse

4-WEEK VARIATION OF CASES (SURINAM:1995-2001)

-50

0

50

100

150

200

250

300

0 5 10 15 20

4-WEEK PERIODS

RE

PO

RTE

D C

AS

ES

1995: cases 129

1996: cases 677

1997: cases 90

1998: cases148

1999: cases 695

2000: cases 1205

2001: cases 760

4-WEEK VARIATION OF CASES (BAHAMAS: 1998)

-50

0

50

100

150

200

250

300

0 5 10 15 20

4-WEEK PERIODSR

EP

OR

TE

D C

AS

ES

1998: cases 336

D

DAu

SE N

Statistical Significance Level of ENSO Associations (* with 1994/95)

REGION El Niño

(N)

El Niño+1

(N+1)

N &N+1 La Niña

Caribbean

(8 Epeds)

88% 64% 92% **

(94%)*

-

T & T

(8 Epeds)

64% 88% 92% **

(80%)*

-

Barbados

(6 Epeds)

74% 74% 90% **

(95%)*

-

Jamaica

(5 Epeds)

53% 80%

(90%)*

79%

(89%)*

-

Results Summary In general, across the region, 19-nineties are observed to be more prone to the epidemic than 19-eighties. There is a periodicity of about 4 to 3 years in the 19-eighties and 3 to 2 years in the 19-nineties with more frequent outbursts. May be due to the fact that, in the 19-ninetees, temperatures were warmer and rainfall was less abundant, for example, as indicated by the anomalies for T & T. These conditions reduce the incubation period and increase the disease transmission rate.

The epidemic shows a well defined seasonality over the region. It occurs in the latter half of the year. The warmer and drier conditions (less abundance in rainfall) appear to trigger the epidemic with the onset of the rainfall, which subsequently & speedily develops. Longer spells of less abundant rainfall and warmer temperatures appear to enhance the probability of the epidemic.

There is a tendency for the spread to get narrower, from south east to north in the region. Perhaps, this may be due to the warmer & moist climate (tropical warm moist climate, more suitable for vector breeding and propagation) that persists in the SE, in contrast to the tropical climate with seasonal rainfall in the central and the nothern part.

The periodicity seen roughly agrees with the periodicity of ENSOs

SYNOPSIS Significance: The work discussed forms a part of the

retrospective component of the AIACC Dengue Project-SIS06. May be stated that, exciting features of the dengue epidemic & evidence of climate influence are seen. Namely;

(i) Periodicity & Seasonality.

(ii) The influence of the temperature and rainfall.

(Iii) Significant association with El Niño episodes (N & N+1 together).

We cannot change the Climate Change!

But adaptation measures could be provided to minimize the impacts

Impacts on Vector

A: Temperature Increase: Increase in numbers, increased frequency of blood meals, and expanded spatial distribution including highland areas. Also increases rate of extrinsic incubation( period lowers)

B: Precipitation: Either increase or decrease in larval habitats (very heavy rainfall could flush out habitats). Humidity increase may increase survival. Flooding, and hence stagnant water, could increase small habitats.

Droughts could result in possible decrease in larval habitats, but storage of water increases

COMMON SCENARIOS

(POTENTIAL BREEDING PLACES)

Possible Adaptation Options

Intensify public awareness through propaganda and education

Devise early warning systems coupled to climate forecasts

Make the public health sector more efficient and effective on issues concerning vector borne diseases (vector control, surveillance, health education)

If socioeconomic (SE) conditions, attitudes and practices are contributing factors, make attempts to improve/change them.

“Public Awareness & Education”

One of the best Adaptation Options:

Examples of Adaptation: Venice Trip in June 2002

CONSTRAINTS

Time series of dengue data spanned only 20 years, which limited the number of ENSO episodes to 4. The influence (+ve or –ve) introduced by migration activities taking place, spraying and serotypes/dengue types have not been considered. Need to complete a socioeconomic & a KAP survey, which presumably will occur soon.