Saudamini Das - Awareness as an Adaptation Strategy for Reducing Health Impacts From Heat Waves...

7/31/2019 Saudamini Das - Awareness as an Adaptation Strategy for Reducing Health Impacts From Heat Waves (Updated)

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AWARENESS AS AN ADAPTATION STRATEGY FOR REDUCING HEALTH

IMPACTS FROM HEAT WAVES: EVIDENCE FROM THE DISASTER RISK

MANAGEMENT PROGRAM*

Saudamini Dasa

Abstract

In recent years, one of the prominent and high frequency calamities due to climatechange has been the mortality and morbidity due to heat waves. A heat wave is defined as

an extended time interval of abnormally hot and humid weather extending from more

than one day to several days. During heat wave, the dissipation of metabolic heat of

human body stops due to the slowing down of the evaporation of body perspiration andbody has to work extra hard to maintain normal temperature. Thus core body temperature

goes up due to heat stress and if it exceeds 40C (tropical areas), the person collapses or

may die. Now this is a global phenomenon affecting both developed and developing

countries and the coastal states of Orissa and Andhra Pradesh are the worst affected onesin India. With changed climate, adaptation is a key and essential strategy to counter the

immediate effects along with long term mitigation measures and one of the adaptivestrategies has been to issue heat wave warning and undertake awareness campaign tobring behavioural changes in people to counter the health impacts.

The state of Orissa has undertaken a massive awareness campaign on dos and donts

during heat waves and this campaign has got intensified under the Disaster Risk

Management (DRM) program of Government of India and United Nations Development

Program that started in 2002. The DRM program is going on in 16 of the 30 districts ofthe state. This campaign aims to bring behavioural changes in people by telling them the

dos and donts during heat wave period and there is report of such strategies reducing

both mortality and morbidity in the state. The present paper is based on some initial

results from the research project that is evaluating this awareness campaign in Orissa inreducing health effects due to heat waves. We use the heat wave index based on

atmospheric temperature as defined by the Indian Meteorological Department andanalyse the death toll for the period 1998 to 2010 using a district level panel data set. The

districts where DRM program is going on are taken as the treatment units and the rest of

the districts as control units. Initial results show the programmed districts to have

witnessed some reduction in death toll compared to non-programmed ones aftercontrolling for heat wave conditions. Though preliminary, these results do indicate that

generating awareness on dos and donts during heat wave period does help people to

change their behaviour and reduce the mortality due to heat stress. The results show theaverage death per year in the state that is witnessing more and more heat wave days in

recent years would have doubled in the absence of the awareness campaign.

*: This paper is based on the ongoing research Adaptation to Heat Waves: Evaluating the role of

Awareness Campaign as an effective strategy to avert health risk financed by South Asian Network of

Development and Environmental Economics (SANDEE). The results are preliminary.

a: Department of economics, SSN College, University of Delhi, Delhi 110036, India and Institute of

Economic Growth, University of Delhi Enclave, Delhi 110007, India. Email:[email protected]
mailto:[email protected]:[email protected]:[email protected]:[email protected]


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AWARENESS AS AN ADAPTATION STRATEGY FOR REDUCING HEALTH

IMPACTS FROM HEAT WAVES: EVIDENCE FROM THE DISASTER RISK

MANAGEMENT PROGRAM

1. Introduction

The global surface temperature is seen to have increased by 0.3Cto 0.6

C during

the last century with 1990s being the hottest decade and 1998, the warmest year with a

temperature of 0.57

C above the 1961 to 1990 average in the last 140 years1

The State of Orissa in eastern India is disaster prone and has the history of

witnessing cyclones, drought and floods very often. A recent addition to the list of naturaldisasters has been the casualties from regular heat waves, though unheard of in recent

history.

(Mohanty,

2006). The warming world has given rise to many weather and climate extremes like

Heat Waves having adverse effect on human health. A Heat Wave is an extended

period of abnormally and uncomfortably hot and humid weather during which the

evaporation of perspiration that cools the human body slows down and the body has to

work extra hard to maintain normal temperature. If core body temperature exceeds 40

C

(tropical countries), heat stroke occurs and causes death.

In recent years, severe heat waves have caused high mortality and morbidity in many

parts of the world (Chicago1995, 1999; most part of Europe 2003; State of Orissa and

Andhra Pradesh in India 1998, 2003 etc). Though human influence has been proved to be

a big contributor to European heat waves (Scott et al. 2004), the impact of climate change

to such tragedies is clear-cut. This scenario is likely to get aggravated in coming years

(IPCC 2007a, 2007b, Kunkel et al. 2008) and WMO predicts the heat related fatalities to

double in less than 20 years. Under the circumstances, adaptation is a key response

strategy to minimize the potential impacts and adverse effects of heat waves on health

(Menne and Ebi 2006).

1.1 State of Orissa and Disaster Management

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With more recent data, 2010 may come out as the hottest year.2

Heat wave is not yet listed as a natural disaster by the central government of India, though the Orissa state

government is strongly and regularly arguing before the central government to recognise heat waves as a

natural disaster now.

Suddenly the state witnessed unprecedented heat waves in 1998, particularly in


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the coastal areas and the administration and people were taken unaware resulting in a

calamity situation. Compared to single digit casualties (range: 1 to 5) due to heat stroke in

previous years, the death toll in 1998 rose to 2042 alarming the state administration (see

table 1). The very next year, the state was devastated by a super cyclone with 356 km per

hour landfall wind velocity and 7 metres of storm surge that killed 10, 000 people and

caused colossal loss of other properties. After these calamities, the state government

formed a special department, called Orissa State Disaster Mitigation Authority (OSDMA)

in the year 2000 to handle the frequent disasters befalling the state.

In 2002, the Disaster Risk Management (DRM) Project of the Government of

India and United Nations Development Program was initiated with the aim of making

sustainable reduction in disaster risk in some of the most hazard-prone districts in select

states in India. This program was implemented in 16 of the 30 districts of Orissa withOSDMA as the nodal agency. DRM continued till 2007 and these districts have been

covered by the Disaster Risk reduction (DRR) Program after that. Awareness generation

was an important feature of DRM project and keeping with this objective, the disaster

preparedness of the state under OSDMA took a paradigm shift by changing the focus

from relief, restoration and rehabilitation to planning, preparedness and prevention.

Generating awareness or knowledge on dos and donts during a disaster has become a

core of OSDMA activities in making people prepared to face a disaster.

Starting 1998, heat waves are a regular feature in the state and monitoring heat waves

and preparing people to face the situation has also become an important and regular

activity of OSDMA recently. One of the main strategies opted by OSDMA to counter

heat attack has been to undertake awareness campaign on dos and donts during the heat

wave period along with the broadcast of heat wave warnings. The awareness campaign

has been in the form of giving precautionary instructions like not moving in empty

stomach, drinking lots of water or ors solution, either avoiding travel during noon or

carrying umbrella or wet towel along with, wearing cotton cloths, keeping ors solutions

with oneself etc. Information is also given on symptoms of heat attack and the subsequent

first aids to avoid serious consequences. Multiple mediums like electronic and print

media, posters, pamphlets etc are being used and the type and number of mediums used

has varied from year to year. Along with this campaign, other activities undertaken are


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giving directions to various government departments to reduce exposure of workers

during peak hot hours, ensuring smooth supply of water and electricity, etc. These other

activities, however, were also being undertaken since 1999 (after 1998 calamity) by the

calamity mitigation committee of the Government of Orissa when OSDMA was not in

existence and are being done routinely since then. The novel addition to heat wave

management under DRM by OSDMA is the awareness campaign that started

systematically since 2003 and is continuing with different magnitude depending on the

intensity of the problem. The mediums used for the dissemination of knowledge on dos

and donts are mostly national medias and people in every district of the state are likely to

get informed depending on their level of literacy (whether read news paper or not) and

affluence (whether own television, radio or not). The DRM districts are expected to have

advantage in this regard as the project is supposed to have trained local people or

volunteers to help disseminate information and has appointed project officers in every

district to continuously monitor and provide feedback during any calamity. Thus we

expect the awareness index (if effective in reducing health effect or changing behaviour)

to have a negative impact on the heat wave deaths for the whole state (as we cover years

before and after the campaign) and a stronger impact in DRM districts compared to the

non-DRM districts. Orissa being a poor and backward state where people are likely to be

less alert on their own, awareness campaign should be more effective in changing

peoples behaviour in DRM districts because of the volunteers and field officers.

Personal communication with OSDMA officers and even some common people indicate

awareness campaign to be very effective and managing heat wave is also being described

as one of the most important and successful disaster management work of OSDMA (as

seen from table 1 as well). If proved true, then changing behaviour through information

campaign can be suggested as an effective adaptation strategy for areas facing heat

waves.

2. Literature Review

There are broadly two categories of studies on heat waves, the first category

falling under the public health literature and the second under impact evaluation studies.

The first set of studies have examined the link between mortality and temperature

anomaly by studying either the daily fluctuations in mortality or the aggregate annual


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death counts and have found a positive association between excess mortality and

temperature, especially when temperature exceeds specified threshold (ONeill et al.

2003; Ramon-Medina and Schwartz 2007; Ramon-Medina 2006; Deschenes and Moretti

2007; Deschenes and Greenstone 2007; Deschenes 2010). Except the last two, the rest of

the studies are based on daily mortality figures. Some of these studies have analysed

individual events and some have used case cross over approaches.

Studies analyzing specific heat wave calamities have found various death risk

increasing factors like being black (live in impoverished neighbourhoods even if have

same income) and having a high school education or less (ONeil et al. 2003), not having

access to air-conditioning (Smoger 1998a), living alone and leaving home regularly

(Semenza et al 1996, Naughton et al 2002) etc. These factors have amplified the effect of

temperature on mortality. Old and people with heart or other health related problems are

also reported to be more vulnerable to heat attacks (ONeil et al. 2009). Davis et al (2003)

is the only study that suggests a decline in heat related mortality over the years due to

physiological adaptation along with the use of air-conditioning.

Using case cross over approach and pooled data, Michelle L Bell and others

(2008) examined 754 291 heat related deaths witnessed in Mexico city, Sao Paulo and

Santiago in between1998 to 2002 and found same and previous day temperature, and

high age to be significantly related to deaths. The significance of factors like education

and sex were found non-robust differing across communities and varying between cities.

Both these categories of studies clearly indicate the existence of socio-economic

gradients for heat related deaths along with factors like age, health history and the

weather.

Undoubtedly health impacts due to heat waves is a culmination of atmospheric,

climatic, environmental, socio-economic, health history as well as awareness related

factors and to identify the role of awareness in averting health risk by changing

behaviour, one has to isolate the role of other factors. There is ample evidence that health

education through mass media or public awareness campaign on environmental quality

brings substantial behavioural changes in people, though it seems to depend on the

structure, timing as well as the soundness of the theoretical content of the campaign

(Cutter and Neidell 2009; Hornik 2001; Randolph and Viswanath 2004). However, there


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is dearth of research on behavioural changes due to public education on heat waves or

heat waves warning and also on how effective has been the behavioural changes in

averting health risk, though educating people of appropriate behaviour during hot weather

are thought to be an effective adaptation strategy by many government (WHO 1990,

Menne and Ebi 2006). Early warning has been proved to be effective in saving lives

during storms and complimentary to other death reducing factors (Das and Vincent 2009)

and there are also evidences from studies doing impact evaluation of state intervention,

mainly heat wave warning and other adaptive information to reduce mortality, that heat

early warning do reduce deaths (Ebi et al 2004, Alberini et al 2008).

Alberini et al. (2008) analysed daily death counts for all non-trauma and other specific

causes covering 86 US counties using regression discontinuity design and found heat

stress to have resulted in appreciable increase in mortality of cardiovascular and

respiratory patients and elderly. They found the heat alerts issued by National Weather

Services (NWS) to have reduced the impact of heat stress appreciably, but the effects

were different across regions of USA. It seemed to reduce excess mortality among elderly

by 25% in mid-west, north-east and mid-atlantics, but neither heat nor heat warning

seemed to have any effect on mortality in the south. Probably this is due to

acclimatisation and behaviour. Ebi et al (2004) assessed the impact of the advanced

Kalkstein heat/health warning system, named after its inventor Dr Laurence Kalkstein, in

early years of its introduction in Philadelphia and found it to have saved 117 lives in

between 1995 and 1998.

The present paper assumes awareness campaign on heat waves to bring

behavioural changes in people and the effect of heat early warning to be complimentary

to awareness campaign as both are done simultaneously and wish to evaluate the

awareness campaign in terms of its effect on reduced mortality3

. The following logic

chart explains the underlying ideas behind this impact evaluation study.

3The full research, however, aims at using a scientifically defined index of heat wave like Heat Wave

Index, Universal Thermal Climate Index (based on durability, temperature and humidity) or Comfort Index

(based on temperature, humidity and wind speed) and bring out a more disaggregated picture of the effect

of awareness on mortality for groups of people on the basis of rural, urban, age, gender, well-being etc.


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Logic Model for Awareness Campaign

Program input Program

output

Program outcome Program impacts

1.Do not go out of home in empty

stomach,

2.Drink lots of water, carry water bottle

and ors solutions

3.Avoid travel during noon

4. Carry umbrella or wet towels

5. Wear light colored cotton cloths

6. Small children, elderly, fat people and

persons with diabetic, blood pressure,

heart problem need extra care.

7. Do not give water to persons becoming

unconscious and consult doctor

8. Avoid alcoholic drinks

__________________________________

Mediums used: Television, News paper,

Radio, Pamphlets, Panchayat Meetings,and volunteers

1. Awareness

on heat waves

2. Awareness

of

consequences

if do not

follow the

instructions.

1. Change in

dietary habits

2. Carry

umbrellas or

cover head with

wet cloths if

traveling during

noon.

3. Less exposure

during noon or

change in work

plans

1. Less mortality

or less hospitalizat

Other factors impacting output and outcome

1. Climatic factors like temperature, humidity etc.

2. Economic well being

3. Relative alertness

4. Other defensive measures (use of air conditioners, water coolers, presence of medical facilities etc

5. Natural adaptation (Vegetation cover, water bodies or wetland area)

The interventions are in the form of telling people what to do or not to do to avoid heat

stroke during heat wave period and this is likely to create awareness in people on heat

stroke, change their behaviour and ultimately the changed behaviour will help reduce the


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mortality. The ultimate effect of these inputs on mortality, however, will depend on other

intervening factors like heat wave conditions, peoples alertness, wellbeing, natural

conditions present etc.

3. Study Area and Heat Wave

The study area for this paper is the whole of the coastal state of Orissa in eastern

India, the Bay of Bengal forming its eastern boundary. The state is economically

backward with more than 7.5 million families, or 47 per cent of the total population of the

state remaining below the poverty line as per the latest 2002 BPL census of the state,

though expert groups are suggesting the government to revise the figure to 84.47 % from

47%. Agriculture and related activities constitute the main occupation catering to more

than 76% of the population and more than 85% of the people live in rural areas, the

percentage of urban population varying between as high as 42.93 for Khurda district to

4.29 for Nayagarh (Census of India, 2002). The average density of population is 236 per

square kilometre for the state and it varies between 666 for Khurdha district to 81 for

Kandhamal district. Thus the coping capacity of people to extreme events is limited and

intervention is necessary at different levels to enable them to get over the extreme

weather events. The state has 30 districts of which 16 have been covered under the

disaster risk management project of Government of India and UNDP (the highlighted

districts in figure 1).

Figure 1: Location of districts covered under disaster risk management program in

the state of Orissa.


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As said before, though unheard of in recent history, the state is witnessing unusual

increase in temperature and severe heat waves since 1998, when 2042 deaths occurred.

Coastal towns and areas are more severely affected than the interior ones and as expected

most of the coastal districts have been covered under the program. The state has 19

weather stations spread all over the state and has been divided into 10 agro-climatic

zones. Using the heat wave definition given by the Indian Meteorological Department

(IMD), the heat wave days are calculated for the state as a whole and for different district

separately. Next the heat wave days are compared with the heat wave deaths4

Table 1 and both figure 2 and 3 show heat related deaths to have gone down over

the years in the state in spite of similar heat wave condition prevailing compared to the

year 1998 (in fact number of heat wave days going up in the state). However, if 1998 is

ignored being an extreme year, the decline in death no longer looks striking (figure 2).

There is decline in death both in DRM and non-DRM districts, may be because of

physiological adaptation as human body does get used to increased temperature to some

extent over time (Davis et al., 2003) or some other factors other than the awareness, but

witnessed

in the whole state as well as in the districts where DRM project is being implemented and

the ones not receiving the program (see table 1, Figure 1 and 2).

The IMD defines heat wave differently for different areas based on the prevailing

normal temperature of the area. The formal definitions are the following:

(i) The normal temperature is < 40 C. Any increase from the above normal temperature

is called heat wave.

+ (5 or 6) C Moderate heat wave

+7 C or more Severe heat wave

(ii) The normal temperature is 40 C. Any increase from the above normal temperature

is called heat wave.

+ (3 or 4) C Moderate heat wave

+5 C or more Severe heat wave

(iii) If the maximum temperature of any place continues to be 45 C consecutively for

two days (40 C for coastal areas), it is called a heat wave condition

4The state has the provision to pay INR10, 000 as compensation for every heat wave casualty. Thus, every

heat wave reported death is examined and certified by a doctor whether the death is due to heat stroke or

not. The paper analyses those deaths which are certified by doctors to be due to heat stress.


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the decline seems comparatively sharp for the DRM districts compared to the non-DRM

ones (figure 3). This makes it an interesting case to find out if the intensity of awareness

campaign did play some role in reducing the heat wave mortality.

Table 1: The number of heat wave days and the number of human casualties

in the state of Orissa

Source: Indian Meteorological Department, Bhubaneswar and Orissa State Disaster

Mitigation Authority, 2010

Year Heat

Wave Days

Deaths

in the

state

Deaths in

DRM

districts

Deaths in

non-DRM

districts

1983 1 3 NA NA

1987 2 1 NA NA

1988 1 22 NA NA

1989 1 1 NA NA

1995 1 9 NA NA

1996 2 3 NA NA

1998 28 2042 1124 918

1999 25 91 57 34

2000 18 29 8 21

2001 12 25 21 4

2002 21 41 29 12

2003 28 67 48 20

2004 8 41 35 10

2005 29 234 161 75

2006 4 21 17 4

2007 8 47 28 19

2008 12 69 41 27

2009 29 85 63 22

2010 38 61 25 35


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Heat wave and human casualties in Orissa

0

50

100

150

200

250

1999* 2000* 2001* 2002* 2003* 2004* 2005* 2006* 2007* 2008* 2009* 2010*

Year

Deathsandnumberofheatwaved

ays

heat_death

heat_days

Figure2: Heat Wave days and number of human casualties in whole of Orissa

Heat deaths in treatment & control districts

0

20

40

60

80

100

120

140

160

180

1999

*

2000

*

2001

*

2002

*

2003

*

2004

*

2005

*

2006

*

2007

*

2008

*

2009

*

2010

*

Year

DeathsinDRM&

non-DRM

districts

death_DRM districtsdeath_nonDRM districts

Figure 3: Heat wave related deaths in districts receiving DRM program and the

ones not under the program


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4. Methodology

The paper uses the Difference-in-Difference (DID) measure to bring out the

impact of awareness generation on death (Pattnayak 2009; Wooldridge 2002; Card and

Krueger 1994). DID is a non-experimental technique to measure the change induced by aparticular treatment or program over a given period of time. The underlying idea is to

examine the effect of the treatment by comparing the treatment group after the treatment

both to the treatment group before the treatment and to some other control group not

receiving the treatment. In program evaluation, the most important step is the

measurement of counterfactual outcome, the outcomes that would occur in the absence of

the policy being implemented. Deducing the effect of the treatment simply by looking at

the treatment group before and after the treatment do not show the real impact as a lot of

other things surely go on at the same time as the treatment and one has to control for it.

The DID method uses a control group to subtract out the effect of these other changes,

assuming that these changers are identical between the treatment and control groups. In

other words, the control group provides the missing information or the outcome for the

treatment group in the absence of treatment, the counterfactual. Along with the

identification and measurement of counterfactual, the other important task is also the

control and careful consideration of co-founders which are likely to be correlated with the

interventions and thus are likely to impact the outcome.

The simplest set up of DID is one where outcomes are observed for the two groups

(A and B) for two time periods. One group (B) is exposed to a treatment in the second

period but not in the first and the second group (A) is not exposed to the treatment at all.

With repeated cross sections, the model for a generic member of any of groups is written

as

++++= dBdddBY *22 1010 (1)

wherey is the outcome of interest. The dummy variable dB (=1 for the treatment group)

captures possible differences between the treatment and control groups prior to the

intervention or policy change and d2 is a dummy variable for the time period. The time

period dummy, d2, captures aggregate factors that would cause changes in y even in the


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absence of a policy change. In equation 1 0 measures the baseline average; 1, the

differences between the two groups in year 1,0

represents the time trend in the

outcome, and1

represents the difference in the changes over time. The policy impact iscaptured by 1 , the coefficient of the interaction term, d2 *dB, which is the dummy

variable equal to one for those observations in the treatment group in the second period.

Thus difference-in-differences estimate is:

)()(1,2,1,2,1 AABB

yyyy = (2)

This simple set up can easily be extended to account for various subsets of

treatment and control group (in which case we include further interaction terms and

measure the difference-in-difference-in-difference), to add covariates to control for

compositional changes in the groups, or use data on multiple time periods and groups etc.

With multiple time periods and groups, the model to be estimated is written as

igtgtgtigtgtgtigt uZXY +++++= , (3)

where i represents individuals (=1, ., gtM ), g represents group, and t, the time. The

full set of time effect is captured by t and the group effects are captured by g . gtX are

the group/time period covariates (the policy variables),igt

Z are the individual specific

covariates,gt

are unobserved group/time effects andigt

u are the individual specific

errors. In most cases, the unobserved group/time effects are ignored and suitable

estimates are chosen depending on the nature of y to get , that measures the policy

impacts. Hartwig and Grimm (2010) estimated such a model for measuring the effects of

2002 food crisis in some part of Malawi on the health of the children using a probit

specification.

With panel data, the model can be written as

ititittitiitittitXMucXWY +++=++++= , (4)


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where i are the units (=1, ., M), t is time (=1, , T), andit

W is the binary indicator that

equals unity if unit i participates in the program at time t. itX are the controls and the

error it is defined to be having a time invariant component ( ic ) and a time varying

component ( itu ) such that itiit uc += . The coefficient is the treatment effect.

Depending on the availability of data, Eq. 4 is estimated either by fixed effect or random

effect using the suitable specification to get the accurate results. Many a times one has to

use a combination of techniques like PSM and DID to control over the biases. The

present paper uses a panel data and estimates the following equation:

ititit

itittitit

it

moredummySeverity

SHWDHWDindexawarenessDNDPpopulation

distpdXdrmdrmdistrictdrmperioddrmTY

++

+++++

+++++=

deg_40_

_

____

1110

98765

43210

(5)

where the variables are the following:

Y: Number of human deaths for the ith district (1, .. , 30) in the tth year (1998, 1999, ,

2010),

T: time trend (to account for the physiological adaptation of human body to high

temperature and other time variant changes),

Drm_period: the treatment period dummy (=1 for 2003 and onwards),

Drm_district: the treatment group dummy (=1 for 16 districts)

Population: ith district population in the year t,

DNDP: ith district net domestic product in the year t (account for well-being as well as

urbanization),

Awareness_index: number of mediums used in the year t to generate awareness (varies

from 1 to 10 in different years),

HWD: heat wave day as defined by IMD,

SHWD: severe heat wave day as defined by IMD,

Severity_dummy: dummy variable for the year t when the temperature deviation has been

more than 10

C (exceptionally hot years) in the ith district as per the temperature

recorded by the nearest weather station or by the one falling in its agro-climatic zone,

40deg_more: total number of days in the year t when the temperature has crossed 40C in

the ith district (too many days with marginally more than 40C temperature may not be


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captured by heat wave days, but may cause heat stress due to continuous high

temperature), and is the error.

Equation 5 is estimated with both fixed effect and random effect using a Poisson

specification because of the count nature of the dependant variable.

4.1 Controlling for endogeneity biases

Heat wave is a climatic phenomena and its impact is dissimilar over areas and over

time. Though the awareness campaign is undertaken regularly starting with the month of

March, its effect gets visible only if the temperature crosses some threshold. Areas with

high heat wave index are likely to show high impact and vice versa. This bias in results is

likely to be controlled as the model is using different measures of temperature deviations

from the normal level which would be capturing the stress on human body from heat

wave for each of the districts.

After every calamity, people do change their behavior and undertake precautionary

measures and such possibilities are also expected after the 1998 heat attack in Orissa.

However, precautionary motive is strongly linked with income and the vulnerable

population facing casualties due to heat wave are mainly the poor and marginal workers

in the state5

Data from multiple sources have been used to estimate the model. Data on human

casualties was collected from OSDMA office where every death, reported to have been

due to heat stroke, has been verified and certified by a medical officer. Daily maximum

temperature data for the months of March, April, May and June (summer months for the

state of Orissa) for the period 1998 to 2010 have been used to measure the different

measures of heat wave index. This data source was the Indian Meteorology Department,

Bhubaneswar. As mentioned before, the criteria suggested by the IMD have been used to

calculate the heat wave and severe heat wave days for different district. The state has 30

who spend little on averting activities. People who adapt to heat waves by

using air conditioners or water coolers are less likely to face casualty. We hope to havecontrolled for this by using the district level net domestic product for different years that

captures the degree of affluence or poverty in the district.

5. Data

5Based on OSDMA report pasted on the web site.


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districts, but only 18 weather stations (see figure 1) and thus, the agro climatic division of

the state was used to identify the weather station that can capture the temperature of the

district more accurately. The temperature of the districts having weather stations within

its boundary was represented by the temperature recorded in the respective weather

stations. The ones not having any weather station within its boundary were represented

by the temperature recorded in the nearest weather station situated within its agro-

climatic zone. Information on mediums used for awareness generation in different years

was collected from OSDMA office. Though various mediums like different radio

stations, television channels, print Medias and posters have been used and have been used

for different duration in different years, the awareness index has been simply taken as the

sum of the mediums used in a year. No weightage has been given to the duration or to the

type of mediums, though their impact is likely to be very different. The same value of

awareness index is used for both drm and non-drm districts as the channels and the news

papers used are viewed and read state-wide depending on affordability.

Population figures for non-census years were interpolated from the 1991 and 2001

census data using district wise decadal growth rates. District level net domestic product

was collected from Planning Commission of India publications. District NDP was

available only for the years 1992-93 till 2004-05 and for years beyond 2004-05,

extrapolated values have been used.

6. Results

The panel data used has 13 years of observations on 30 districts of which 16 districts are

under drm, 14 are non-drm and of the 13 years, five are pre-drm years and eight are post

drm years. The summary statistics of the variables are reported in Table 2.

Table 2: Summary Statistics

DRM districts

(n= 208)

Non_DRM districts

(n= 182)Variables Mean (std

error)

Min (Max) Mean (std

error)

Min (Max)

Heatstroke_

death

7.97

(26.78)

0 (254) 7.50

(32.79)

0 (367)

40deg_more 15.82

(15.82)

0 (77) 27.6

(20.19)

0 (77)

HWD 5.67

(6.57)

0 (38) 8.71

(7.77)

0 (38)


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SHWD 3.07

(4.53)

0 (19) 4.55

(5.58)

0 (25)

Severity_dummy 0.15

(0.36)

0 (1) 0.143

(0.356)

0 (1)

Population 1622734

(691239)

510199

(3674529)

910553

(471163)

260349

(2118176)

DNDP 242658(174255)

39193(955390)

129040(94361)

24588(585983)

Awareness_index 5.38(7.93)

0 (23) 5.38

(7.93)

0 (23)

The summary statistics show the average death per year per district to be little

different between the drm and non-drm districts (7.97 and 7.5 respectively), but the non-

drm districts to have been suffering from more severe heat attack, the average value of all

the measures of heat wave being higher for these districts. These districts are also less

populous and poor (the district net domestic product is nearly half of that of drm districts)

compared to the drm districts. The vulnerable districts for implementation of drm project

were decided on the basis of total number of hazards that included floods, droughts,

cyclones and heat waves and thus, some of the poor districts more severely affected by

heat wave may have been left out of the project.

Two different specifications of equation 5 described above has been estimated with both

gamma distributed random effect and district fixed effect. The dependant variable,

number of deaths, being a non-negative count, Poisson estimates are used for estimatingthe equation 5. In one specification, the variable awareness index is used along with other

variables and program impact is measured by interaction of the drm_period dummy with

drm_district dummy and the awareness_index. In the other specification, the program

impact is measured from the interaction of the drm_period and drm_district dummy

without the awareness index. Both the specifications, as observed from table 3 and 4

confirm the drm program, through intensive awareness generation, to have reduced heat

related death in Orissa significantly.6

6

The results are only preliminary.

Of course, the second specification without

including the awareness index shows the program impact to be much higher and this

could be because of the reason that the awareness index has not been defined carefully in

the model. The main activity to control heat stroke has been the awareness campaign and

probably, it needs to be defined more carefully to capture the full impact of the intensive


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drm interventions on heat wave. Both random effect and fixed effect estimates give

similar results and confirm the positive effect of drm program in reducing extreme health

impact from heat waves.

Table 3: Random and Fixed effect Poisson estimates to explain Awareness

Campaign effect on heat stroke death (Dep variable= number of human death dueto heat stroke)

variables Gamma distributed random

effect Poisson estimates

District fixed effect

Poisson estimates

Drm_period 2.13*** (0.144) 2.12*** (0.144)Drm_district 0.02 (0.53) -----Drm_pdXdrm_dist ----- -----Awareness_index 0.086*** (0.009) 0.086*** (0.009)Drm_pdXdrm_distXawareness_index -0.018** (0.008) -0.018** (0.008)40deg_more 0.042*** (0.003) 0.043 *** (0.003)

HWD 0.007 (0.006) 0.007 (0.006)SHWD 0.058*** (0.006) 0.059*** (0.006)Severity_dummy 0.553*** (0.065) 0.542*** (0.066)Time_trend -0.735*** (0.027) -0.736*** (0.028)Population 1.37e-06*** (4.21e-

07)1.48e-06** (6.05e-07)

DNDP 1.81e-06*** (4.53e-

07)

1.78e-06*** (5.01e-

07)Constant 0.853* (0.46) -----Wald chi2 5528.03 ,

Pro>chi2=0.00

5512.04,

Prb>chi2=0.00lnalpha

0.389 (0.227)Alpha 1.476 (0.335)Loglikelihood ratio test of alpha=0 Wald Chibar2 =

2624.93,Pro>chibar2=0.00

N=390

Table 4: Random and Fixed effect Poisson estimates to explain DRM project effect

on heat stroke death (Dep variable= number of human death due to heat stroke)

variables Gamma distributed

random effect

Poisson estimates

District fixed effect

Poisson estimates

Drm_period 2.033*** (0.0151) 2.03*** (0.151)

Drm_district 0.274 (0.552) ---

Drm_pdXdrm_dist -0.634*** (0.1136) -0.635*** (0.1165)Awareness_index ---- -----


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Drm_pdXdrm_distXawareness_index ---- -----

40deg_more 0.055*** (0.003) 0.055*** (0.003)HWD 0.009 (0.006) 0.009 (0.006)

SHWD 0.043*** (0.006) 0.043*** (0.006)

Severity_dummy 0.683*** (0.063) 0.666*** (0.064)

Time_trend -0.630*** (0.022) -0.629*** (0.024)Population 1.15e-06*** (4.28e-

07)

1.08e-06*** (6.05e-

07)

DNDP 3.26e-06*** (4.31e-

07)

3.30e-06*** (4.63e-

07)

Constant 0.482 (0.476) ------

Wald chi2 5407.28 (Pro>chi2 =

0.00)

5394.00 (Pro>chi2 =

0.00)

lnalpha 0.439 (0.225)

Alpha 1.55 (0.35)Loglikelihood ratio test of alpha=0 Chibar2(01)=

2767.21, Pro >chibar2 = 0.00N=390

Notes: ***, **, * imply level of significance to be 1%, 5% and 10% respectively.

Figures in parenthesis show standard errors.

Most of the other variables show coefficients on expected lines. All the different

measures of heat wave have positive and significant coefficient and the severity dummy

(years or areas when the temperature increase deviates from the normal temperature by

10 or more degrees) seems to be the biggest killer. As expected, people get used to high

temperature in course of time and time trend has a negative and significant coefficient.

Against expectation, the district net domestic product has a positive and significant

coefficient implying that richer districts are more vulnerable to heat stress. This could be

because of activities where workers are more exposed, increased urbanisation leading to

unhealthy surrounding and congestion etc.

Though preliminary and based on macro data, the results show the heat related deaths in

Orissa could have doubled in the absence of the drm program that used awarenessgeneration to control heat stroke.

7. Conclusion

Due to global climate change, the heat wave occurrences have become more regular,

intense and wide spread in both developed and developing countries. Along with long

term measures like changing housing structure, town planning, increasing green cover


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etc, short run adaptation measures like warning, health education, advices on precautions

etc are resorted to by policy makers to counter the impacts, but the effectiveness of these

strategies is not researched. The paper analysed the deaths due to heat waves in Orissa

and evaluated the impact of intensive awareness campaign undertaken by the Orissa State

Disaster Management Authority in some of the districts of the state falling under the

Disaster Risk Management Program of Government of India and United Nations

Development Program. Under DRM program, the most important strategy to control heat

wave impact has been to make people aware of the dos and donts during heat wave

period. We found significant reduction in death in DRM districts and this could have

been because of the fact that the DRM districts have witnessed more intensive awareness

generation than the non-DRM districts. Results show the heat stroke casualties in the

state could have doubled in the absence of this intervention. Though the research is

ongoing and the paper quotes some preliminary initial results, the findings have

significant policy implications. The problem of heat wave being recurring, the policy

makers all over the world are looking for effective and economical strategies and

awareness campaign could be one such adaptation measure.

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