Use of climatic data for improving epidemiological surveillance

Centre For Public Health Intervention TechnologyNational Institute Health Research And Development

Global warming and Climate change

1. Introduction

The increasing greenhouse gasses: CO2: 31% CH4: 51% N2O: 17%

Earth mean surface temperature: Increase 0,2-0,4 ºC / decade

Sea level: Predicted to rise 15-95 cm at

2100

Source:IPCC

Source : WHO; climate change and human health 2003

Vector borne diseasesmalaria, dengue, filariasis,

Water borne diseasesdiarrhea, cholera, typhoid

Air borne diseasesISPA, asthma, influenza & peny sal nafas lainnya (ekspos

cemaran udara indoor, emisi & embient) Malnutrition Food borne diseases Others:

Cardio cerebral vascular diseases, hypertensionMental disordersInjuries (from extreme weather events)

● Malaria● Hemorrhagic fever● Cholera/diarrhea

Every year:● Undernutrition kills 2,7 million people● Diarrhea kills 1,8 million people● Malaria kills 1,1 million people

Source : WHO, 2007

1) How much is the health impact of climate change in Indonesia?

2) What efforts have been made in climate change adaptation for the health sector?

3) What is the appropriate system/model that needed in adaptation against climate change impact on health?

MODEL DEVELOPMENT

MODELAPPLICATION

Identifikasi Risiko

Early survey in several provinces• Climate• Air quality• Water quality and

quantity• Vector borne

disease• Digestive tract• Respiratory tract• Nutritional

problem

DATABASELINE • Surveillance

• Adaptation• Health service• Environmental

health efforts• IVM• Fast response• Emergency

responseetc.

• Surveillance• Adaptation• Health service• Environmental

health efforts• IVM• Fast response• Emergency

responseetc.

• Effort• Impactetc.

EVALUATION

Step 1 : Data baseline survey in “kabupaten” and city in 6 provinces

Objective : Get the disease patterns due to climate change in Indonesia

Result : Availability and completion of demography, climate, and

disease data for 5-12 years Routine data of risk factors, such as air quality, water

quality, and vector (no routine data, collected in just few spots so it can’t represent overall area condition)

The recording of many disease case based on varies characteristic and age group cause some difficulties in analysis

Information about Climate Change Result NotesKnowledge about CC and its impact to health

40% Vector borne disease

Participation in CC socialization 15%

Socialization done by Health Department

15%

Participation in CC adaptation action 15% Socialization, promkes

Availability of CC adaptation in planning

15%

Identification of CC-related disease 30%

Availability of special unit dealingwith health impact of CC

15%

Climate data Availability of climate data varies in every subdistrict

and city, but could be completed from province and national level

Available up to daily data

Demography data Availability of demography data couldn’t reach 15 years

period (average: 7-10 years) Variation in age grouping data Annual Total Fertility Rate data is not always available

Air pollution data Every sub district and city doesn’t have any

regular data spot check Not available in some years Data spreads in many agencies: Regional

Development Planning Agency , Ministry of Environment , Regional Health office , university, etc

Data available only in certain area Data is not recorded in the PROFILE or

regional statistics

Clean water data No regular data sample test Type of data varies between districts / cities:

coverage, type of facilities, improve/unimprove

Data spreads in many agencies: Regional Development Planning Agency , Ministry of Environment , Regional Health office , university, etc

The data vector borne diseases (malaria, dengue, chikungunya, Ispa, etc.):

Not available in some areas, depending on the number of cases / endemicity

Available between 7-10 years Some data are only available yearly (not monthly), eg.

API Greatly depending on whether or not the recording

system of reporting Data msebar disease in bebara agencies Variations in the age grouping

SURVEILANS TEMUAN LAPANGAN

Data source Poli, Pustu, Posyandu

Diagnosis Clinical (malaria: LAB)

Data collectingRecapitulation of report

Data processing and analysisCompilation, deskriptif

ReportingMonthly, weekly

Human resources Concurrent tasks, program officer

Training That not all been trained

Supporting laboratoryNot all districts / cities have supporting laboratory

Ability for the diagnosis Partly constraints diagnosis

0.48 0.67 0.60

2.33

0.66

0.48

0.69

415

162

272

433 447

798

110

0

100

200

300

400

500

600

700

800

900

(0.50)

-

0.50

1.00

1.50

2.00

2.50

1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11

2005 2006 2007 2008 2009

C.hujan

Insiden

malaria

Tahun/Bulan

Insiden malaria C.hujan Linear (Insiden malaria) Linear (C.hujan)

0.48

0.72 0.93

2.33

0.86 0.69

0.35

28.5 28.2 28.4

28.1 27.227.5

29.3

15

17

19

21

23

25

27

29

31

(0.50)

-

0.50

1.00

1.50

2.00

2.50

1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11

2005 2006 2007 2008 2009

Suhu

Insiden

malaria

Tahun/Bulan

Insiden malaria Suhu Linear (Insiden malaria) Linear (Suhu)

5.1

8.8

23.2

14.9698

475368

525

306

874

473

0

100

200

300

400

500

600

700

800

900

1000

-5.0

0.0

5.0

10.0

15.0

20.0

25.0

1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11

2003 2004 2005 2006 2007 2008 2009

Curah

hujan

IR

DBD

Tahun/Bulan

IR DBD Curah hujan Linear (IR DBD) Linear (Curah hujan)

26.9

25.9

27.9

23.5

24.0

24.5

25.0

25.5

26.0

26.5

27.0

27.5

28.0

28.5

-0.5

0.0

0.5

1.0

1.5

2.0

2.5

3.0

3.5

1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11 1 3 5 7 9 11

2003 2004 2005 2006 2007 2008 2009

Suhu

Insidens

DBD

Bulan/Tahun

Insidens DBD Suhu rata-rata Linear (Insidens DBD) Linear (Suhu rata-rata)

• Availability and completeness of demographic data, climate and disease ranged from 5-12 years

• Unavailability of air quality regular data in each district /city

• No available vector data • Existing data have not been processed and

analyzed in an optimal • From the available data (limited), the trend of

climate variability in the incidence of disease is not 'consistent'

Objective: Obtain a model of health impacts of climate change surveillance

Specific objective1. To determine the variables needed in the surveillance

system on the health impacts of climate change.2. To identify the stakeholders who play a role in the

development of surveillance systems.3. To obtain a model of health impacts of climate change

surveillance.

F

E

E

D

B

A

C

K

FOLLOW UP

ANALYSIS & DATA INTERPRETATIONweekly

DATA MANAGEMENTweekly

REPORTING

DATA SOURCESRecording and reporting

( weekly)

DATA COLLECTIONweekly,

•Deseases1 Malaria2 Dengue3 Diarhea4 Pneumonia5 ILI

6 PTM

•Fakt. Risiko7. Curah hujan8. Suhu9. Kelembaban

• Fakt. Risiko1. Malnutrisi2. Vektor3. Kenaikan muka air laut4. Disaster

Result1 Graphic2 Distribution3 Trend4 Mapping (GIS)

•SubditSurveillance•BMKG•Community Health Centre•Health Distrct Office•Subdit Surv.eillance•Loka BTKL•BMKG Station•Miniistry of Env.

1) Rapid asessment (identification

surveillance)

2) Surveillance Model Formulation

3) Surveillance software development

Climate DateRainfall, Temp

(BMKG*)

Overlay Deseases Vs climate

(District level)

DeseasesDataSurveillance

(Center)

Data Import Deseases

climate(NIHRD)

Overlay Deseases Vs climate

(Province level)

Overlay Deseases Vs climate

(National level)

Data Processing and Analysis

GraphicGIS

Early WarningAdaptation Strategy

12. Alur

1) The regions of endemic diseases and specific health problems2) Regional 'vulnerable' (National board of climate change)

2) Areas where the EWARS program has been running

1) Trial of surveillance systems on the health

impacts of climate change

2) Development of Modelling Prediction

Dengue Incidence In Indonesia