MAPPING OUT VULNERABLE AREAS AND POPULATION DUE TO ADVERSE HEALTH IMPACTS OF CLIMATE CHANGE IN VIETNAM

In the scope of the activity, we only considered the frequency of the climate related phenomena, without considering the intensity, as well as the area affected and duration of the events (such as for storm and flood level, the intensity of droughts, area affected and the duration, etc). Weighted of each indicator are shown in Table 1. Then, applying standardized formula (2) and formula (3) to calculate the exposure indicators.

MAPPING OUT VULNERABLE AREAS AND

POPULATION DUE TO ADVERSE HEALTH IMPACTS

OF CLIMATE CHANGE IN VIETNAM

FINAL REPORT

MAPPING OUT VULNERABLE AREAS AND

POPULATION DUE TO ADVERSE HEALTH IMPACTS

OF CLIMATE CHANGE IN VIETNAM

Hanoi, 01 – 2011

Organization structure

Technical and financial supporting agencies:

World Health Organization (WHO)

Vietnam Health Environment Management Agency (VIHEMA)

Assoc Prof Nguyen Van Hoang, PhD., independent consultant Assoc Prof Nguyen Vo Ky Anh, PhD., independent consultant Assoc Prof Le Khac Duc, PhD., independent consultant Assoc Prof Luong Xuan Hien, PhD., RCRPH

Duong Chi Nam, MD., VIHEMA Nguyen Bich Thuy, MPH., VIHEMA Phan Thi Thu Hang, MPH., VIHEMA Pham Son Tung, BA., RCRPH

Doan Trong Trung, MPH., RCRPH Nguyen Van Thinh, MPH., RCRPH Trinh Huu Hiep, BA., RCRPH Trinh Hanh Phuc, BA., RCRPH

Table of contents

1 Introduction 1

2 Overview on climate change 1

3 Study methods 6

3.1 Definitions and concepts 6

3.2 Methodology for mapping vulnerability 7

4 Study results 11

4.1 Exposure map 11

4.2 Health sensitivity map 18

4.3 Health adaptive capacity map 24

4.4 Vulnerability map due to the CC impacts on health 29

4.5 Disasters damages and health outcomes map 33

5 Conclusions and recommendations 40

5.1 Conclusions 40

5.2 Recomendations 40

References 42

VIETNAMESE REFERENCES 42

ENGLISH REFERENCES 44

Annexes 47

Annex 1: Maps of basic indicators 47

Annex 2: Figures of basic indicators 52

List of tables

Table 1 Weights of exposure indicators 13

Table 2 Exposure scores of all 63 provinces/cities 14

Table 3 Exposure of all 63 provinces/cities in groups 17

Table 4 Health sensitivity scores of all 63 provinces/cities 20

Table 5 Health sensitivity scores of all 63 provinces/cities in groups 23

Table 6 Indicators and weights of health adaptive capacity 24

Table 7 Scores of health adapative capacity of 63 provinces/cities 25

Table 8 Scores of health adaptive capacity of 63 provinces/cities in groups 28

Table 9 Weights of component indicators of the vulnerability to CC 29

Table 10 Scores of vulnerability of 63 provinces/cites 29

Table 11 Scores of vulnerability of 63 provinces/cites in groups 32

Table 12 Indicators and weights of disasters damages and health outcome of 63 provinces/cities 33

Table 13 Scores of disasters damages and health outcomes of 63 provinces/cities 35 Table 14 Scores of disasters damages and health outcomes of 63 provinces/cities in groups 38

List of graphs

Graph 1: Diagrammatic representation of the direct and indirect pathways by which

climate change affects human health .4

Graph 2: Steps for making vulnerability and its component maps 8

Graph 3 Exposure scores of all provinces/cities in graph 15

Graph 4 Exposure map to CC 16

Graph 5: Indicators and weights in health sensitivity map 20

Graph 6 Health sensitivity scores of all pronvinces/cities in graph 21

Graph 7 Health sensitivity map 22

Graph 8 Scores of health adaptive capacity of 63 provinces/cities in graph 26

Graph 9 Health adaptive capacity map 27

Graph 10 Scores of vulnerability of 63 provinces/cites in graph 31

Graph 11 Health vulnerability map 31

Graph 12 Scores of disasters damages and health outcomes of 63 provinces/cities in graph 36

Graph 13 Disasters damages and health outcomes map 37

Graph 14 Heat wave map 2009 48

Graph 15 Cold wave map 2009 48

Graph 16 Percentage of poor households map 2008 49

Graph 17 Percentage of households using clean water 2009 50

Graph 18 Percentage of households having hygienic latrines 2009 51

Graph 19 Figure of percentage of poor households 2008 52

Graph 20 Figure of percentage of households using clean water 2009 52

Graph 21 Figure of percentage of households having hygienic latrines 2009 52

Abbreviations

CC Climate change

HAC Health adaptive capacity

IPCC Intergovernmental Panel on Climate

Acknowledgement

The Research Center for Rural Population and Health would like to express our sincere thanks to the VIHEMA – MOH, WHO’s office in Vietnam for their

technical and financial support in Mapping out vulnerability areas and

population due to adverse health impacts of climate change in Vietnam

We would also like to thank the agencies and departments directly under MOH, General Statistics Office, Ministry of Natural Resources and Environment, Ministry of Agriculture and Rural Development, Departments of Health and Preventive Medicine Centers in 63 cities/provinces for their enthusiastic cooperation in data collection process

Our deep appreciations go to the experts and researchers for their critical and constructive comments and enthusiastic contributions in this activity

Assoc Prof., Trinh Huu Vach, PhD

Director of Research Center for Rural

Population and Health

1 Introduction

Nowadays, climate change (CC) is widely known as global warming Climate stimuli are refered to changes in climate in a long period of time, including the increase in temperature, changes in rainfall and the rise of extreme weather phenomena such as storm, flood, drought and ice melt in the polars and on mountains, together with sea level rise CC has threatened the human life Vietnam is one of the most affected countries by CC [32] During the last 30 years, sea level in Vietnam has increased 5cm As predicted, the sea level will rise 9cm by 2010, 33cm by 2050, 45cm by 2070 and 1m by 2100 [6] In addition, Vietnam will suffer the growing impact of natural disasters caused by

CC, especially storms and floods which are increasing in frequency and intensity

There has been a limited number of studies on CC in Vietnam Some studies and assessments of CC impacts and adaptation were conducted, especially in flooding areas However, applying of mapping to assess vulnerability due to

CC health impacts of CC is still new

To provide policy makers in health sector an intuitive view on how the health problems manifest in different provinces throught out the countries, in order to make priorities and appropriate intervention Research Center for Population and Health (RCRPH) collaborated with Vietnam Health Environment Management (VIHEMA) under the support of World Health Organization

(WHO) has been conducting an activity titled “Mapping out vulnerable areas

and population due to the impacts of climate change in Vietnam”, which held

from May to December 2010 This activity was implemented towards two objectives, which are (1) Initially design a data set of basic climate and health indicators related to CC impacts in 63 provinces/cities of Vietnam, (2) Map vulnerable areas and communities due to health impacts of CC in Vietnam and (3) propose recommendations following activities for better assessments on health impacts of CC in the future

2 Overview on climate change

According to the United Nations Framework Convention on Climate Change (UNFCCC), CC is defined as “changes in climate which directly or indirectly caused by human activities, to change the atmosphere in a period of time, besides natural climatic changes.” [42]

There were two viewpoints on the causes of global warming The first opinion indicated that the temperature increase is caused by the greenhouse effect, more emphasis on the cyclic warming of the Earth due to endogenous activities The other opinion that human activities causing the increased the concentration of CO2 and the other gases, creating greenhouse effect which agreed by the majority of scientists This is almost the absolute cause of CC and thereby cause sea level rise [26]

The increase in concentrations of greenhouse gases in the atmosphere warms the Earth’s surface During the period 1996–2005, Earth's surface temperature increased by 0.74°C The Intergovernmental Panel on Climate Change (IPCC) estimated that CO2 emissions increased 4 times and temperatures will rise 2-

6oC by 2100 compared with the previous period of industrialization [26] According to the latest report of IPCC in 2007, in the lower scenario, the average global temperature will able to rise about 1.8oC (1.1 – 2.9oC), in the higher scenario that would be 4oC (2.4 to 6.4°C) [30]

Based on some possible scenarios, the Third Assessment Report of the IPCC showed the impacts of sea level rise that was increase in coastal erosion, surges caused by storms and floods, extending coastal flooding areas, and changes in the quality of surface water and groundwater The report also mentioned the loss of lands and important components of coastal ecosystem, the increasing risks of flooding towarding loss of resources and cultural values, impacts on agriculture, tourism, transportation, etc

Regarding the impacts on ecosystems, IPCC reported that approximately 30% of plants and animals species were at risk of extinction, if Earth temperature increases from 1.5 to 2.5°C compared wi th the average of 20 years of the late 20th century [11] CC creates the ecological crisis in the affected areas, causing many native species in degraded and even in disappearance [11]

20-In agriculture, due to water shortages and raging heat wave, the grains and corn yield will be reduced by 30% in Asia and Africa countries [35]

CC not only influenced on the social security of a country, but also among

nationalities During the CC conference in Bali in 2007, Mr Hans Ioakhim Shelhuber predicted that CC would create waves of refugees There will be about 500 million people worldwide forced to leave their homeland to move to less affected areas Moreover, the direct impact of extreme weather phenomena will lead to enormous increase of the diseases by insects and micro-organisms such as malaria The research on the consequences of CC conducted by WHO in 2002 resulted that there will be at least 150,000 people die each year, mostly in the Third World countries They can die of diarrhea, malaria and other infectious diseases, heart disease or malnutrition

According to a study by the Institute of HydroMeteology and Environment of Vietnam, the country has suffered the effects of climate change Specifically, the annual average temperature has increased about 0.1oC per decade, even

in summer that would be from 0.1 to 0.3oC per decade The sea level has risen about 2.5 - 3cm in the past decade [15] Heavy rains have occured more often

in some regions, causing severe floods, while declined in other areas, causing droughts Path of storms has gradually moved to the Southern Central and the South, and seemed to happen in the last months of a year The World Bank has published a list of the most severely affected countries due to climate stimuli In which, Vietnam was among the countries most severely affected by storms, floods and rising sea levels [31]

Coastal region of Vietnam has a high potential for economic development In Vietnam, over half of urban areas, 60% of population, the majority of industrial parks, export processing zone for aquaculture, port, marine and tourism are belong to coastal areas [9] According to a CC scenario, sea level rise will be able to threaten the habitat and living conditions of 17 million people Fertile lands, seafood farms and fisheries might be lost, forcing to coastal communities to resettle This would increase pressure on the remaining areas, increasing deforestation, biodiversity reduction, as well as social pressures such as housing, social security and employment Besides, this would lead to the increasing effects of saltwater intrusion, especially in Cuu Long Delta (Ho Chi Minh City, Ca Mau, etc) and rising intensity of coastal erosion, such as in Canh Duong district, Quang Binh province which is 56m per year [15]

Climatic changes also affects human health in many different aspects Prof Anthony J McMichael of the Australian National University formed a model of the pathways by which CC affects human health, directly and indirectly [1]

Graph 1: Diagrammatic representation of the direct and indirect pathways by which climate change affects human health

CC directly affects on health through extreme weather events, air pollutions, etc Heatwaves inversely influence on human health, especially elders People with respiratory, cardiovascular and mental problems are also sensitive in case

of teperature increase [1] In addition, CC along with its abnormal expressions such as prolonging cold spells that will affect people’s tolerance and their routines

The increase in intensity and frequency of natural disasters such as storms, tornados, floods, droughts, heavy rains and landslides, etc will increase the number of deaths and indirectly affect human health through pollutions, malnutrition and diseases Consequences of these disasters may give a burden to daily lives of people, influencing both their physical and mental health The most vulnerable subjects will be farmers, the poor, ethnic minorities in mountainous areas, the elderly, women and children [1]

CC can cause water and food scarcity overtime According to IPCC, estimated

by 2080, 1-3 million people worldwide will lack water due to CC impacts [30] If

Physical system

(river flows, ocean temperature, soil mosture, air quality)

Biological cycles Ecological linkages Ecosystem function

Climate

Change

impacts

Economic impacts

Infrastructure, production, trade, GDP

Indirect impact –

ecologically mediated

socially mediated

Direct impacts

(extreme events,

heatwaves, air

pollutants, etc)

the situation last, it will cause negative effects on health such as malnutrition and infectious diseases The water-borne diseases are expected to rise due to the lack of drinking and living water

Increase in temperature tends to increase the possibility of some tropical diseases Growth and development rate of many bacteria, insects and contaminants will be becoming faster [1] In example of malaria transmitted by mosquitoes, the change in average temperatures in areas where mosquitoes usually live will lead to changes in the habitats of these species Thus, malaria may occur in areas that have not been there

3 Study methods

3.1 Definitions and concepts

This study uses some definitions and concepts related to CC as follows:

Vulnerability Vulnerability is the degree to which a system is susceptible

to, and unable to cope with, adverse effects of climate change, including climate variability and extremes Vulnerability is a function of the character, magnitude, and rate of climate change and variation to which a system is exposed, its sensitivity, and its adaptive capacity [28]

Exposure The nature and degree to which a system is exposed to

significant climatic variations [28]

Sensitivity Sensitivity is the degree to which a system is affected,

either adversely or beneficially, by climate variability or climate change The effect may be direct (e.g., a change in crop yield in response to a change in the mean, range, or variability of temperature) or indirect (e.g., damages caused

by an increase in the frequency of coastal flooding due to sea level rise) [28]

Adaptive

capacity

The whole of capabilities, resources and institutions of a country or region to implement effective adaptation measures [28]

Storm Storm and tropical depression are referred as tropical

cyclone, which has the strongest wind around the center as

of level 6 or more (above 39km/h) In forecast, they distinguish that: When the strongest wind around the tropical cyclone center were levels 6-7 (from 10.8 to 17.2 m/s), it is called a tropical depression; when it is level 8 or

higher (above 17.2 m/s), it is called a storm [16]

Flood Flood is a phenomenon that water level rises in a certain

time, then gradually decreases [16]

Drought Drought is an unusual phenomenon that an area is dry in a

long time, without heavy rain or no rain There are 3 ways to

understand drought: 1) Meteorological drought: rainfall less than the average in the period of time, 2) Agricultural

drought: lack of moisture for a period of service or product

average output; 3) Hydrological drought: when water level in

basements, rivers, reservoirs is lower than the statistical average Within the scope of the acivity, meteorological term is used [16]

3.2 Methodology for mapping vulnerability

CC influences differently on different regions, economic sectors and population groups Thus, it is possible to build vulnerability maps for the subjects On that basis, the policy makers can plan and work out policies to reduce the impacts

of CC on areas and sectors in a harmonious and balanced way

According to IPCC, vulnerability can be defined as a function of exposure, sensitivity and adaptive capacity:

Vulnerability = f (exposure, sensitivity, adaptive capacity) (1) From that viewpoint, the vulnerability map due to CC impacts can be built from

3 component maps, which are: 1) Exposure map (as known as climate hazards map); 2) Sensitivity map; and 3) Adaptive capacity map

Based on the IPCC approach to make the above maps, we could calculate how each area and population vunerable to CC, aim to mapping vulnerability [22]

The following step of adopting approach to build the maps were creating basic indicators and collecting data Then, the data were standardized to calculate scores of the indicators These scores were the basis of component maps, as well as the general map – vulnerability map for all 63 provinces/cities The

process to choose indicators, collect the data and finally define scores and make the maps are decribed as follows:

Graph 2: Steps for making vulnerability and its component maps

• Creating a set of indicators

A technical working group was created including experts on geology and maps, water and sanitation, and health to conduct methodology of the study and map vulnerabilty

The experts reviewed documents, chose indicators related to vulnerability to

CC and finally offered a set of indicators by holding group discussions The selected indicators had to (1) represent vulnerability to CC impacts on health and (2) be collectable

• Collecting, entering and checking the data

Weigh the component indicators

(2nd time)

Map vulnerability

to CC

Make component maps (exposure, sensitivity, health adaptive capacity)

Calculate and standardize vulnerability scores

(3rd time)

Based on the comprehensive set of indicators, the working group built tools to collect all the information in all the 63 provinces/cities through out Vietnam in the period 2007-2009

The investigators of RCRPH were divided into groups to collect the data from available documents (such as Statistical Yearbooks, reports, articles, Internet), central agencies, and Provincial Preventive Medicine Centers [2, 5, 6, 17, 18,

19, 21] In which, the data were collected from the documents which published

or reported by the related agencies

Microsoft Excel was used for data entry All the data set was described in tables and classified in groups for easier and more comfortable calculation and mapping

• Standardizing the data

The collected data consist of many types of data calculated in different units

We used the following standardization formula to adjust values so we can calculate among different types of data:

Min i

Max i

Min i j i j i

X X

X X Z

−

−

in which Z i,j is the standardized value of indicator i of province j (Z i,j=0÷1, which

means Z ranged between 0 and 1), X i,j is the value of indicator i of province j,

X i Max and X i Min are the minimum and the maximum value of the indicators of all

63 provinces/cities, respectively

Accordingly, all the data might be adjusted to 0÷1

• Determining weights and scores

In order to build the component maps as well as the vulnerability map, it is

needed to determine weight of each indicator F i (in which i is number of basic indicator, i=1,2,…,n if we have n indicators) Weight may refer to importance of

the factor (indicator in this study) to CC The more related indicator to CC, the higher its weight would be Prescriptively, total of weights of indicators in the same group should be equal to 1 ( 1

The values of component and vulnerability indicators are calculated as:

1 , ,

in which C 1,j is the value of basic indicators of province j

Applying standardization formula (2), we can calculate values of basic

indicators C1 and C1=0÷1

As we got C1, then applying stacking maps formula (3) to make all three

component maps, including 1) climate hazards map (C1=0÷1); 2) sesitivity map

(C2=0÷1); 3) health adaptive capacity (HAC) map (C3=0÷1)

Similarly, the vulnerability map could be built based on the three component

maps, with the corresponding weights F I (I=1,2,3 và 1

F ) After that,

standardizing the vulnerability values C (2), we had C=0÷1

Thus, with the applying of the IPCC methodology on vulnerability and

formulas, we could make the vulnerability maps related to CC Within the scope of the activity, the study group built the maps with the most recent data

in 2009 Some 2009 data is still not available, so we used the data in 2008 and

2007 In the future, updates of the maps can be done with the same method The investigators got the data set mostly from the central agencies and the available documents due to its convenience and it saved time The other data were collected from Provincial Preventive Medicine Center, as the centers’ officials in charge of sanitation and epidemiology could easily master this task and help gather the necessary information

From some discussions, the selected indicators used to determine exposure of each province due to CC impacts were:

• Percentage of type-1 communes (coastal, island, fishing port, river

mouth) (Project number 52, issued together with the Document No

775/TCDS-DA52 by General Office for Population and Family Planning

on the review of project areas for population control [13]): the province which has a high proportion of Type-1 communes tends to be more vulnerable to CC, such as storms, floods, sea level rise [38]

• Percentage of mountainous and highland communes (according to

the data of Committee for Ethnic Minority Affairs [2]): the higher proportion of mountainous and highland communes a province has, the

more adverse impacts the province will deal with (such as floods and land slides)

• Number of storms: a province with a higher frequency of storms is

considered a highly exposed to CC

• Number of floods: Similarily, assessing provinces’ exposure to floods is

done by measuring the frequency of the floods occured within the province

• Number of droughts: besides storm and flood, drought is a clear

manifestation of CC due to changes in temperature and rainfall Number

of droughts occured in an area presented the intensity of CC impacts on the area

• Number of cold waves (below 10 o C): An obvious manifestation of CC

is the abnormal and prolonged cold waves Low air temperature will influence livestock, crops and human health The extreme cold waves are related to low air temperature period, which is characterized by the lowest temperature during the day In lowland areas, extreme cold waves occur when the average temperature of 13oC or less during the day, damaged cold waves occur when the average temperature is less than or equal to 11oC For mountainous areas, the values may be lower

Therefore, in this study we used lowest temperature less than or equal

• Number of heat waves (above 37 o C): Climatic changes also cause

heat waves World Meteorological Organization has set the temperature threshold to affect people is that when the air temperature is greater than or equal to 33oC The temperature increases, the more negative impacts on health Heat level is based on the highest temperature during the day When it is from 35 or 38oC or higher, the day that is

considered hot Within the scope of the activity, the temperature above

In the scope of the activity, we only considered the frequency of the climate related phenomena, without considering the intensity, as well as the area affected and duration of the events (such as for storm and flood level, the intensity of droughts, area affected and the duration, etc) Weighted of each indicator are shown in Table 1 Then, applying standardized formula (2) and formula (3) to calculate the exposure indicators

Table 1 Weights of exposure indicators

No of cold waves (below 10oC) 0,15

No of heat waves (above 37oC) 0,15

This study used MapInfo Professional software for mapping In which, the values were grouped and each group were displayed in a particular color on the map There were three common methods of grouping:

- Standard deviation grouping:

This method divides values into groups, two contiguous groups apart from each other a distance equal to the standard deviation This method is consistent with the normally distributed data set

- Natural break grouping:

The method is to divide values into groups so that the values in the same group have least different and the different between groups are highest

- Maximum break grouping:

This method studies separated values and groups similar values By the method, values are arranged from small to big The groups cut at the point where the different between two adjacent values highest In this study, we used maximum break method

The scoring results of all the 63 provinces’ exposure to CC are described in Table 2 and Graph 3 We apllied maximum break grouping method to divide

values into 10 groups, showed in Table 3 Graph 4 is the exposure map, in which the higher exposed province to CC was showed bolder

Table 2 Exposure scores of all 63 provinces/cities

Ba Ria - Vung

Dak Nong 0.40 Nam Dinh 0.63 Quang Tri 0.95

Graph 3 Exposure scores of all provinces/cities in graph

Graph 4 Exposure map to CC

Table 3 Exposure of all 63 provinces/cities in groups

provinces/

cities

Provinces/cities

Tri; 4) Quang Ninh

4) Lam Dong; 5) Thanh Hoa; 6) Ninh Binh

1) Hai Phong; 2) Son La; 3) Lang Son; 4) Tuyen Quang; 5) Cao Bang; 6) Thua Thien

- Hue; 7) Ha Giang; 8) Phu Yen

Lao Cai

Gia Lai; 5) Nam Dinh

1) Hoa Binh; 2) Bac Giang; 3) Thai Nguyen; 4) Ha Noi; 5) Bac Kan; 6) Quang Binh; 7) Kon Tum; 8) Lai Chau

Hai Duong; 5) Kien Giang; 6) Da Nang

1) Ben Tre; 2) Vinh Phuc; 3) Tay Ninh; 4) Dak Lak; 5) Can Tho; 6) Long An; 7) Dak Nong; 8) Dong Nai

1) Soc Trang; 2) Bac Lieu; 3) Tra Vinh; 4) Vinh Long; 5) Ba Ria - Vung Tau; 6) An Giang; 7) Ha Nam

1) Bac Ninh; 2) Hung Yen; 3) Binh Duong; 4) Hau Giang; 5) Ho Chi Minh City; 6) Dong Thap

The group of highest exposure to CC (scores ranged from 0.91 to 1) included

4 provinces (Quang Ngai, Quang Nam, Quang Tri and Quang Ninh) They were all the coastal provinces, in which 3/4 provinces were in the Central Vietnam The second highest exposed group has the scores ranged from 0.77

to 0.91, including the provinces of Nghe An, Binh Dinh, Ninh Thuan, Lam

Dong, Thanh Hoa and Ninh Binh They are mainly located on the Central Coastal of Vietnam, usually suffered from severe storms In deed, the region has the most harsh climate of Vietnam, with prolonged heat waves and droughts in dry seasons and floods in rain seasons

The group has the lowest scores of exposure included Bac Ninh, Hung Yen, Binh Duong and Hau Giang provinces In the 4 provinces, two provinces are in Red River Delta and the other two are in Cuu Long Delta, where has favorable terrain and little affected by extreme weather events

4.2 Health sensitivity map

The study considered some related factors to human health sensitivity affected

by CC The sensitivity were mainly determined by the following indicators:

• Population density: areas with high population density are more

sensitive to CC, in other words, those areas have larger number of people will be affected by CC impacts

• Percentage of rural population: the socio-economic conditions in rural

areas are generally lower than urban areas Therefore, the provinces have the high proportion of rural people will be highly sensitive to CC In addition, according to World Bank, agriculture is one of the most affected sector by CC [31]

• Percentage of female population: women is considered more

susceptive to CC than men because of their biological and social characteristics Specifically, lack of water source and infectious diseases cause big problems for women On the other hand, women often have more difficult than men when dealing with natural disasters and epidemics caused by CC [40]

• Percentage of poor households: poor households have limited money

for their expenditures and poor living conditions, so they are vulnerable subjects for adverse impacts of CC

• Percentage of underweight children aged under 5: underweight

children are very sensitive to the climatic changes, especially the increase in temperature or abnormal cold waves Moreover, natural disasters lead to hunger and diseases, adversly affecting the health of underweight children at this age

• Percentage of children aged under 1 not having full vaccination:

Children aged under 1 are easy to be affected by some infectious diseases such as tuberculosis, diphtheria, whooping cough These diseases have a tendency to increase with the current climate trend If vaccinated, children can easily immune the diseases [20]

• Percentage of households not using clean water: Impacts of CC

cause water scarcity If utilization rate of clean water in a locality is not high, the local people will face obstacles in sanitation and health, as well

as responding to climate change

• Percentage of households not having hygienic latrines: unhygienic

latrines are as sources of environmental and water pollution, increasing the risks of food-borne and water-borne diseases, particularly diarrhea Unhygienic latrines with water shortages are the factors facilitating the consequences related to CC and health becomes more severe

In the process of selecting indicators, the indicators "Percentage of

households using clean water" and "Percentage of households having sanitary latrines" are considered "inverse" factors to the sensitivity to climate change,

due to people using clean water and sanitary latrines are often easier to deal with the consequences of CC Therefore, to facilitate the calculation process,

the working group used the indicators "Percentage of households not using

clean water" and "Percentage of households not having hygienic latrines"

With similar reasoning, we also use the indicator "Percentage of children under

age 1 not having full vaccination”

Weights of the indicators were discussed and proposed as in Graph 5 Percentage of households using water and unhygienic latrines are highest weighted two indicators by 0.20 as the significant causes of people illnesses Also, the changes in weather due to climate change leading to the unusually cold waves, heat waves and floods increasing the pollution The poverty rate has the weight of 0.15 as this factor directly determines the quality of life of people as well as the ability to pay in case of sickness, diseases and response

to the adverse impacts of climate change Other indicators are weighted 0.10, except for children under 1 years old not having full vaccination with the weight

Graph 5: Indicators and weights in health sensitivity map

Then, we applied standarized methods for scoring the sensitivity indicators for each province/city Health sensitivity map to climate change are shown in Graph 7 with maximum break grouping The health sensitivity indicators of all

63 provinces/cities are also presented in detail as below:

Table 4 Health sensitivity scores of all 63 provinces/cities

Dak Lak

0.36

Thai Nguyen 0.54 Long An 0.71 Quang Nam 0.86

Graph 6 Health sensitivity scores of all pronvinces/cities in graph

Graph 7 Health sensitivity map