Advertisement

Managing the Increasing Heat Stress in Rural Areas

  • Adithya PradyumnaEmail author
  • Ramkumar Bendapudi
  • Dipak Zade
  • Marcella D’Souza
  • Premsagar Tasgaonkar
Living reference work entry

Abstract

Increasing temperatures are likely to impact human health. An increase in severe heat wave days and heat mortalities has been observed in India over the past few decades. At present, there is little evidence on the heat exposure, impact, and the adaptation measures in the rural context. The present study examines vulnerability of rural communities to heat stress in the semiarid villages in Maharashtra state of India.

The study was conducted in five villages of Jalna and Yavatmal districts of Maharashtra. Household survey covering 20% of the households was conducted in Jalna during 2016 summer months. Twenty data loggers were installed to measure the indoor temperatures in Yavatmal.

Exposure to heat in various circumstances, both outdoors and indoors, was reported. Age, gender, wealth, and pre-existing health conditions were significantly associated with occurrence of heat-related symptoms (HRS). Exposure factors such as working outdoors during midday, roofing material, and indoor ventilation were significantly associated with occurrence of HRS. The indoor temperature in houses with tin roofs was found to be higher as compared to cement-roofed houses.

Existing coping strategies appear to be inadequate to protect people from outdoor and indoor heat stress. People from poorer households reported socioeconomic and livelihood challenges in adopting coping strategies as well. A long-term and locally appropriate strategy in terms of knowledge about HRS and infrastructure and access to timely medical facilities is needed. Development of effective surveillance mechanism and a comprehensive state-level heat action plan are needed to prevent and monitor heat mortalities in the future.

Keywords

Heat stress Heat-related symptoms Differential vulnerability Rural health Semiarid regions 

Background

The risk of heat-related illnesses and deaths is likely to increase due to rising temperatures (Intergovernmental Panel on Climate Change 2014). In India, there has been an increasing trend of heat wave-related deaths in the last few decades. A report by National Disaster Management Authority highlights that between the periods 1992 and 2015, about 22,562 heat-related deaths were reported (Government of India 2016). In fact, heat stroke was the second major cause of deaths from natural disasters in India (accounting for 15% of those deaths) during the decade 2001–2012 (Paul and Bhatia 2016). Most studies on the health impacts of heat have been conducted within the context of officially declared heat waves. It has been found that significant mortality and morbidity can be attributable to heat exposure. The effect could be up to 43% increase in deaths due to heat wave in urban India (Azhar et al. 2014).

Future climate projections for India indicate that heat waves will likely be more intense, have longer durations, and occur more often and earlier in the year. Intensification of heat waves will also lead to increased mortality rates (Dholakia et al. 2015; Murari et al. 2015). It was also found that death attributable to extreme heat was about the same as moderately high temperatures (Gasparrini et al. 2015).

Increasing heat exposure is linked to occupational health risks and negatively impacts work productivity (Dash and Kjellstrom 2011; Kjellstrom et al. 2009). Studies on occupational heat stress impacts in India found that decreased productivity was reported most commonly among outdoor/semi-outdoor occupations with high workload (e.g., brick manufacturing, metal fabrication, construction, etc.), whereas productivity losses were reported less frequently among indoor workers (Venugopal et al. 2015).

An association between high temperature and noninfectious disease mortality was found in a study conducted in rural western India. Men in working age involved in outdoor activities like agricultural and industrial workers were more vulnerable to heat (Ingole et al. 2015).

In Gujarat and Rajasthan, workers in the rural and semi-urban industries of ceramics, pottery, iron works, and stone quarry were found to be vulnerable (Nag et al. 2009). In the rural context, it was found that among the rice farm workers in West Bengal, high workplace heat exposure caused heat strain and reduced work productivity (Sahu et al. 2013).

Apart from decreased work productivity, increased temperatures also have other social impacts. A 21-year rural longitudinal survey conducted in Pakistan showed that men move out of the village due to extreme heat stress and that the landless and asset-less poor are more likely to do so (Mueller et al. 2014). A large-scale study conducted in Thailand found that working under heat stress conditions is associated with worse overall health and psychological distress (Tawatsupa et al. 2010).

Apart from outdoor heat stress, exposure to hot indoor conditions is also a concern. In a study assessing the impact of heat wave events on dwellings (Quinn et al. 2014), it was found that a substantial fraction of houses exceeded dangerous heat thresholds during extreme events. Older adults staying indoors during hot indoor temperature conditions are at a risk of significant detriment of physical functions (Lindemann et al. 2017). There is a need to improve awareness regarding management of indoor heat stress. The use of passive building design strategies such as shading, thermal mass, and internal air movement has been suggested to mitigate the overheating of dwellings and to postpone the use of active systems (Din and Brotas 2017). It has also been demonstrated in an experimental study that simple and effective hybrid passive cooling system can significantly help in reducing thermal loads of roofs (Ponni and Baskar 2015).

A recent study from urban India identified exposure (geographic location, housing characteristics, and occupational and behavioral factors), susceptibility (age, pre-existing health status, and socioeconomic factors), and adaptive capacity (access to health services and information, coping mechanisms, and societal factors (infrastructure, information, and social capital) determine vulnerability to heat (Tran et al. 2013). Pre-existing conditions, such as cardiovascular diseases, may be exacerbated by heat stress (Khan et al. 2014). However, there is little evidence of the heat experience, impact of heat exposure, and adaptation measures to heat and heat waves in the rural context.

Most of the above studies examine vulnerability to heat stress due to work exposure (occupational hazard) and focused on urban areas. Very few studies have attempted to understand the factors contributing to vulnerability of the communities. In this context, this study examines vulnerability of rural communities to heat stress in villages located in semiarid regions of Maharashtra state in India. An exploratory qualitative study was conducted to inform this survey (Mhaskar et al. 2016). The specific objectives addressed by this study are:
  1. (i)

    To quantify heat-related symptoms and illnesses in the rural communities during summer time

     
  2. (ii)

    To identify the categories of the rural population that are most affected by heat stress

     
  3. (iii)

    To understand exposure to outdoor and indoor heat stress

     
  4. (iv)

    To identify factors contributing to vulnerability to heat stress

     
  5. (v)

    To examine various existing strategies being used to cope with heat stress

     

Methods

Study Location

The study was conducted in Jalna and Yavatmal districts of Maharashtra. Jalna is located in the central part of Maharashtra state in northern Marathwada region. The district has a sub-tropical climate with average annual rainfall ranging between 650 and 750 mm. The district experiences years of drought with annual rainfall recording as low as 400 mm. The hot dry summer season is from March to June. During summer, the maximum day temperature ranges between 42 °C and 43 °C (Government of India 2018). The summer months are dry, with relative humidity generally between 20% and 25% in the afternoon.

The blocks of Jafrabad and Bhokardan in Jalna district were selected as there was anecdotal evidence of few deaths due to heat stress in 2014. Exploratory visits were undertaken during the months of April and May 2015 to select the villages. The factors considered for selecting the villages were sparse vegetative cover, lack of access to water, and remoteness. Three villages were purposively selected for the study, namely, Adha and Sindi in the Jafrabad block and Goshegaon in the Bhokardan block. In Goshegaon village, the local government authorities had supplied drinking water in tankers during summer of 2015. Village Adha had a primary health subcenter in the village, whereas the nearest government healthcare system for Sindi and Goshegaon villages is located at a distance of 15 km and 7 km, respectively.

For understanding indoor temperatures, Sonurli and Ekhlara villages located in the Ralegaon block of Yavatmal district were selected for measuring indoor temperatures using data loggers. According to the India Meteorological Department, when the temperature reaches 45 °C, it is considered as heat wave condition. In both Jalna and Yavatmal districts, the maximum summer temperature ranges between 42 °C and 43 °C, thus nearing the heat wave conditions. Figure 1 shows the location of Jalna and Yavatmal districts and of the study villages.
Fig. 1

Location map of Jalna and Yavatmal districts

Sample Survey

Households in each village were categorized according to socioeconomic criteria based on a participatory wealth ranking exercise. Wealth ranking exercise is a participatory tool for classifying the households based on the indicators related to land ownership, asset ownership, food security, migration, and sources of income. The households are classified as very poor, poor, middle class, and better off.

From each of the three villages, a sample of 20% of total households was selected for survey. Stratified random sampling was used for selecting the respondent households. Accordingly, the sample households were selected in proportion to the number of households in the respective wealth categories. In total, 215 households contributed to the detailed household survey (Table 1).
Table 1

Socioeconomic status of sampled households

Wealth category of household

Total households in the study area

Households included in the study

Very poor

226 (21.9%)

46 (21.4%)

Poor

420 (40.7%)

87 (40.5%)

Middle class

241 (23.3%)

51 (23.7%)

Better off

146 (14.1%)

31 (14.4%)

Total

1033 (100%)

215 (100%)

Information collected from the respondents included the following: living conditions (housing structure, access to water for drinking and domestic use, access to electricity), work profile (type of livelihood activities, exposure to outdoor heat), health problems (pre-existing health conditions, self-reported heat-related symptoms, access to medical facilities, and sources of information on preventive measures), and coping strategies used to manage heat exposure and impacts. This list was informed by the literature and also based on our exploratory qualitative study conducted prior to this survey (Mhaskar et al. 2016).

Monitoring of Indoor and Outdoor Temperatures

The selection of household for installation of data loggers was made based on types of roofing. Twenty indoor digital temperature data loggers were installed, inside the houses in the room where maximum time was spent by household members. Twelve of these loggers were installed in houses with tin/galvanized sheet roof structure, 7 loggers in houses with cement concrete roofs, and 1 in a tiled roof structure. Each data logger had a unique serial number embedded within its firmware, allowing for tracking of deployed loggers. The data loggers and the automated weather station were set to record temperatures at a 10-min interval, allowing for a maximum monitoring period of summer months.

An automated weather station was also installed in the village for measuring the location-specific outdoor temperature during the summer months.

Data Collection

A structured questionnaire was developed and pretested. Individual interviews were conducted in the month of May 2016, which is the peak summer period in Maharashtra. An adult household member was selected as the respondent who provided information regarding all other members of the family (in the context of exposure to heat and heat-related symptoms).

The types of heat-related symptoms (HRS) included in the survey are as follows: small blisters or pimples, dry mouth, fatigue, leg cramps, heavy sweating, intense thirst, rapid heartbeat, headache, leg swelling, paranoid feeling, swelling of face, fever, diarrhea, vomiting, hallucinations, and fainting. The occurrences of these were recorded with a recall of 2 months (the hottest months – April and May, during the year 2016). Hallucinations and fainting were considered as severe HRS, whereas the rest were considered as mild HRS for analysis. This list was informed from literature and also from the exploratory qualitative study conducted prior to this survey.

Sample Characteristics

The sampled households comprised of 1224 individuals in total. Out of these, there were 671 male members and 553 female members (accounting for 55% and 45% of total individuals, respectively). About 64% of the total household members were in the age groups of 15–30 years and 31–59 years.

About 18% of persons interviewed belonged to households that were very poor and 37% to households that were poor (based on wealth ranking). The majority of the households belonged to forward caste category (62% of total sample households) followed by scheduled caste category (23%). Illiteracy is prevalent among 25.6% of individuals covered by the survey. About 37% of individuals reported wage labor (agricultural and nonagricultural) as the major summer occupation (Table 2). About 23% of the individuals indicated farming as an important livelihood source. Non-income generating activities such as household chores, education, and other such activities were indicated by 22% of the total individuals from the sample.
Table 2

Sociodemographic characteristics of the study population

Variable

Number of individuals (% of total, n = 1224)

Gender

 Male

671 (54.8)

 Female

553 (45.2)

Age

 0–4

104 (8.5)

 5–14

212 (17.3)

 15–30

450 (36.8)

 31–59

330 (27.0)

 60+

128 (10.5)

Wealth ranking

 Very poor

223 (18.2)

 Poor

448 (36.6)

 Middle class

335 (27.4)

 Better off

218 (17.8)

Caste category

 Scheduled caste

276 (22.8)

 Scheduled tribe

57 (4.7)

 Nomadic and denotified nomadic tribes

44 (3.6)

 Vimukta Jati nomadic tribes

13 (1.1)

 Other backward classes

66 (5.4)

 Forward caste

757 (62.4)

Education

 Illiterate

286 (25.6)

 Primary school

210 (18.5)

 Secondary school

450 (39.8)

 High school

122 (10.8)

 Graduate and above

54 (4.8)

Summer occupation

 Non-income generating activities

205 (22.2)

 Farming

211 (22.9)

 Agricultural and nonagricultural labor

342 (37.0)

 MGNREGS work

113 (12.2)

 Others

52 (5.6)

Data Analysis

Descriptive statistics and cross-tabulations were first used to understand the data. The occurrence of at least one HRS in the individual was the health outcome of interest. This health outcome was cross-tabulated against every other variables (all of which relate to susceptibility, exposure, and coping strategies), through which odds ratios (OR) were calculated on the lines of multinomial logistic regression. Confidence intervals (CI) and p-values for the odds ratios were also reported to help with the interpretation. All the ORs presented are unadjusted.

For analyzing the temperature data generated from each data logger, Hoboware software was used. The software allowed the temperatures to be plotted on a continuous graph covering the monitoring period.

Results and Discussion

Heat-Related Symptoms Among the Households

Among the heat-related symptoms experienced by the household members, headaches, heavy sweating, fatigue, intense thirst, dry mouth, and small blisters/rash were found to be the most commonly reported symptoms (Graph 1).
Graph 1

Frequency of reported heat-related symptoms

Of all individuals, 46.2% experienced at least one HRS during the study period. On average, at least two individuals were affected per household (Table 3). Individuals from 64.5% households experienced fever, and 21.5% households experienced diarrhea during the study period.
Table 3

Occurrence of heat-related symptoms (HRS) at individual level

Particulars

Number of individuals (N = 1224)

Total individuals experiencing at least one HRS (%)

566 (46.2)

Average individuals affected in each household

2.64

Heat stress categories

No HRS (%)

658 (53.8)

Mild HRS (%)

523 (42.7)

Severe HRS (%)

43 (3.5)

Figures in parenthesis indicate percentage to total

Vulnerability to Heat Stress

The analysis is based on the framework given by Tran et al. wherein heat vulnerability is conceptualized as a function of interacting biophysical and socioeconomic determinants that can be broken down into heat hazard probability as well as factors associated with population exposure, susceptibility, and adaptive capacity (Tran et al. 2013) (Fig. 2).
Fig. 2

Framework for assessing heat vulnerability. (Adapted from Tran et al. 2013)

Susceptibility Factors

Susceptibility factors included age, gender, and pre-existing health conditions. The analysis of wealth status has also been discussed here for convenience, though it relates to all dimensions of vulnerability through various pathways.

Elderly population (>60 years old) were more affected as compared to children under 4 years (OR 0.16; CI 0.09–0.29) and those between 5 and 14 years (OR 0.23; CI 0.15–0.30) and those under 30 years of age (OR 0.55; CI 0.37–0.82). Only adults between 30 and 59 years of age were more affected (OR 1.86; CI 1.22–2.85) than the elderly (Table 4). This is also the age group where higher percent of individuals (71.2%) reported heat-related symptoms. It has been reported in literature that elderly and children (Li et al. 2015; Lundgren et al. 2013; McGeehin and Mirabelli 2001; Oudin Åström et al. 2011) are more susceptible to heat stresses. Our finding indicates that “exposure” might be driving the health impact in our study region, rather than “susceptibility.”
Table 4

Occurrence of HRS in relation to demographic variables (univariate analysis)

Parameter

Odds ratio (unadjusted)

Lower CI

Upper CI

p-value

Age

0–4

0.16

0.09

0.29

<0.001

5–14

0.23

0.15

0.37

<0.001

15–30

0.55

0.37

0.82

0.003

31–59

1.86

1.22

2.85

0.004

60+ (ref)

1

   

Gender

Male (ref)

    

Female

0.75

0.6

0.94

0.012

Pre-existing health conditions

None (ref)

    

At least one pre-existing health condition

6.34

4.16

9.66

<0.001

Wealth categories

Very poor (ref)

    

Poor

1.07

0.78

1.48

0.676

Middle class

0.67

0.48

0.94

0.021

Better off

0.52

0.36

0.77

<0.001

Both men and women reported suffering from HRS. However, the proportion of men reporting HRS (49.5%) was relatively more as compared to women (42%). The unadjusted odds ratio for this comparison was 0.75 (CI 0.6, 0.94). This could be due to a greater proportion of men working outdoors during the day (76% vs. 71%, respectively), performing strenuous work (41% vs. 36%, respectively), and use of protective clothing by women, and not due to physiological susceptibility among men.

Those belonging to middle-class category (OR 0.67; CI 0.48–0.94) and better off families (OR 0.52; CI 0.36–0.77) were less affected as compared to the very poor. Higher proportion of individuals from the “very poor” and “poor” categories reported at least one HRS (about 51% and 53% of total individuals in respective categories) as compared to the individuals in the “better off” category (only 35%). Our finding corroborates previous literature which have indicated that people with low socioeconomic status have been reported to be more affected by heat stress (Harlan et al. 2006; Li et al. 2015). The mechanisms through which poverty might make individuals more susceptible include general health condition and working and living conditions.

Among the 1224 total individuals in the sample households, 157 individuals (about 13% of the sample) reported to be suffering from at least one pre-existing health condition (ranging from asthma to cancer). Those with at least one pre-existing chronic health condition reported relatively high HRS (OR of 6.34, CI 4.16, 9.66) (Table 4). Individuals with pre-existing health conditions have been reported to be more susceptible to heat stress (Li et al. 2015; McGeehin and Mirabelli 2001).

The majority of the sample population in the study area belonged to general and scheduled caste categories. The difference in proportion of individuals reported HRS among the two social categories was not statistically significant.

Exposure Factors

Exposure can be influenced by hazard factors, amplifying factors, and protective factors (Tran et al. 2013). In this study, the hazard factors are the outdoor and indoor temperatures; the amplifying factors include outdoor work during peak heat hours, type of occupation, and roofing material; and the protective factors include the coping strategies being employed. In this section, the key amplifying factors are discussed.

Exposure to heat was reported both outdoors and indoors. While the working adult population was exposed to direct sunlight outdoors, the elderly and children were exposed to hot indoor environments during midday and afternoons (Mhaskar et al. 2016). Those from the poorer sections and from the lower castes were disproportionately exposed based on their daily activities and roofing material used for the houses (Photo 1).
Photo 1

Villagers working at MGNREGS sites during summers. (Source: WOTR)

Exposure due to Livelihood Activities

One of the amplifying factors affecting exposure was the type of occupation an individual is engaged in. Majority of the individuals from the sample households (72%) were engaged in outdoor livelihood activities during summer months such as farming on own land, wage labor (agriculture, non-farm), and in the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS), a government employment scheme.

Those engaged in wage labor (whether privately employed or through the governmental MGNREGS scheme) were more affected as compared to those engaged in activities such as household chores and education. Those engaged in farming (OR 2.05, CI 1.38–3.03), labor activities (agriculture and non-agriculture) (OR 3.35; CI 2.33–4.80), and MGNREGS (OR 3.71; CI 2.28–6.04) were more affected as compared to those performing non-income generating activities (Table 5). Occupation (especially physically intense occupations) is a known risk factor for heat-related illness (Centers for Disease Control and Prevention 2011; Jackson and Rosenberg 2010). People perceived those working outdoors (farm laborers) and the elderly and young children stay indoors in heat-trapping tin houses to be more exposed to heat. It was also perceived that those working outdoors during daytime, especially women laborers, are most vulnerable to health impacts of heat exposure. Exposure during travel to work was also reported by many local respondents (Mhaskar et al. 2016).
Table 5

Occurrence of HRS in relation to other variables (univariate analysis)

Parameter

Odds ratio (Unadjusted)

Lower CI

Upper CI

p-value

Summer occupation

Non-income generating activities (ref)

    

Farming

2.05

1.38

3.03

<0.001

Agricultural and nonagricultural labor

3.35

2.33

4.80

<0.001

MGNREGS labor

3.71

2.28

6.04

<0.001

Others

1.95

1.06

3.61

0.03

Minutes spent performing activities outdoors during peak heat hours

0 (ref)

    

1–90 min

1.56

1.00

2.44

0.050

91–180 min

2.88

1.90

4.35

<0.001

181–360 min

1.75

1.14

2.69

0.010

Exposure

Type of roof

Tin (ref)

    

Cement slab

0.69

0.52

0.92

0.011

Others

1.96

1.22

3.13

0.005

The work day was divided into four segments – morning (4 am to 11 am), midday (11 am to 3 pm), afternoon (3 pm to 5 pm), and evening (5 pm to 7 pm). Of these, midday and afternoon together were considered as “peak heat hours” based on local people’s opinion (Mhaskar et al. 2016). The total number of individuals exposed to peak heat hours for 1.5–6 h is 428 (55%) (Table 6). Majority of these individuals were engaged in physically strenuous work such as wage labor (47.9%) and farming (20%). About 14.7% worked as MGNREGS laborers during peak heat hours (Graph 2). Respondents from over 83% households reported regularly using synthetic clothing during summer, which may add to heat-related discomfort.
Table 6

Time spent outdoors during peak heat hours

Time spent outdoors during peak heat hours for work or activities

No. of individuals

0

195 (25.4)

<1.5 h

145 (18.9)

1.5–3 h

255 (33.2)

3–6 h

173 (22.5)

Total individual responses

768 (100)

Graph 2

Occupation of individuals working outdoors for more than 1.5 h during peak heat hours (N = 428)

Occurrence of HRS was relatively much higher in individuals spending more time outdoors during peak heat hours (Table 5).

Exposure During Other Household Chores

Apart from the livelihood activities, other household activities could also result in outdoor exposure to heat. Over 96% households collect firewood from field and forest. In 43.7% households, only women take the responsibility for collection of firewood. About 84% of households indicated spending more than 4 h each week on this activity. The preferred time for this activity was in the morning, early evening, and evening (these are times with relatively less heat outdoors (Mhaskar et al. 2016)).

On an average, during summer, the time taken for collecting water was 71 min (against 40 min during other seasons). In over 93% households, the women were primarily responsible for fetching water. But during summer, in some households men also support women in fetching water. Similar to the practice of collecting firewood, water too is collected during morning, early evening, and evening hours.

Type of Roofing

Another amplifying factor of exposure is the type of shelter or dwellings. A large proportion of households (about 74% of sample households) were living in tin-roofed houses. Tin roofs get heated quickly during daytime as compared to cement slab roofs, but they also cool up rapidly as compared to the cement roofs. About 47% of respondents residing in tin-roofed houses indicated at least one HRS as compared to 38% living in cement slab houses (Table 7). Cross ventilation helps in dissipating heat. However, only 13% of the households reported having cross ventilation in all rooms. Those living in households with cement roof were less affected as compared to those with tin roof (OR 0.69, CI 0.52–0.92) (Photo 2).
Table 7

Roofing material

 

Total individuals living in those houses

Total individuals who experienced at least one HRS

Proportion of individuals experiencing at least one HRS

Tin

887

412

47.0

Cement slab

260

99

38.1

Others

82

52

63.4

Photo 2

Tin-roofed households in the villages. (Source: WOTR)

Without adequate rest and recovery time from heat, people become more vulnerable to heat stress (Khan et al. 2014). Poor design of houses with inadequate ventilation (lack of windows) could exacerbate the condition due to high daytime and nighttime temperatures. The elderly were also perceived as vulnerable due to their physiological condition, with several reported cases of fatigue, dehydration, and loss of consciousness, despite majority of them staying back at home (Mhaskar et al. 2016).

Monitoring of Indoor Temperatures

Graph 3 depicts the indoor temperatures of the houses in Sonurli village recorded using data loggers for the period of 10th of May to 5th of June 2017. On average, as the day progressed, the indoor temperature increased and peaked at around 2 pm. Among the three different roofing types, tin roofs get heated the most (having the highest diurnal maximum and minimum temperatures). During the peak heat hours (11 am–5 pm), the indoor temperature in tin-roofed houses was also more than the outdoor temperatures. Elderly, children, and women who rest indoors during morning and afternoon hours may be at risk for heat stress. Local elderly people felt indoors were “intolerable” after 12 noon. Some of them reportedly rest just outside their houses under the shade of a tree or a portico (Mhaskar et al. 2016).
Graph 3

Average indoor and outdoor temperatures during the whole day (period of 10 May–5 June 2016)

During the day the indoor temperatures recorded in cement-roofed houses were consistently less than the outdoor temperatures, but during the night the temperatures in cement houses were more than the outside temperatures. These houses heated up relatively slowly, but they also cool down slowly. These characteristics of houses and their respective indoor temperatures have important implications for health vulnerability, especially with cooler nighttime temperatures being important for recovering from daytime heat exposure.

Coping Strategies and Adaptation Measures

Individuals, families, and the community took various steps to cope with heat and its impacts. These include changing work timings, resting under trees, resting between working hours, planned reduction in work, using fans indoors, staying hydrated, eating appropriate foods, and using protective clothing. These address the immediate problem and are mostly employed during hot days or following them (Mhaskar et al. 2016).

The different strategies used to cool the houses were as follows: use ceiling fans, use water coolers, spread layer of crop residue over the roof, or a combination of these (Mhaskar et al. 2016). Table 8 shows the difference between the indoor and outdoor temperature when different coping strategies were employed by the households. It indicates that irrespective of the coping strategy employed, houses with tin roof get more heated as compared to houses with cement roof. Even after employing some kind of coping strategy, the temperature inside tin-roofed households was higher by over 4 °C as compared to outdoors. In those houses where water cooler was used, the indoor temperatures were found to be somewhat lower as compared to outside (in cases of both tin- and cement-roofed houses, respectively). Thus using water coolers may provide some relief. However, many times erratic electricity supply and inadequate water availability prevent the households from using these coolers. In addition, coolers were found in less than 4% of study households. There is no clear indication about what might be the best option for roofing and other housing-related aspects. The choice of the roof material also depends on the economic condition of the household. It might be a combination of things including using hay or crop residue over the roof and improving indoor ventilation. Respondents from poorer families reported that tin roofs were useful during the rainy season as compared to thatched roof (Mhaskar et al. 2016) indicating the need for holistic understanding before making knee-jerk recommendations on addressing the challenge of heat stress.
Table 8

Indoor and outdoor temperature difference when coping strategies employed by households

Time of the day

Temperature difference (indoor to outdoor) (°C) when different coping strategies are employed

Tin roof+fan

Tin roof+fan+layer of crop residue

Tin roof+cooler

Tin roof+cooler+crop residue layer

Cement slab+fan

Cement slab+Cooler

Tile +cooler

11 am

2.8

−3.3

−3.4

−0.5

−5.4

−4.7

−0.9

12 pm

3.5

−4.2

−4.3

−1.1

−6.8

−5.6

−1.0

1 pm

4.0

−4.8

−5.1

−2.2

−7.4

−6.1

−1.6

2 pm

3.9

−4.7

−5.8

−4.1

−7.3

−6.9

−2.7

3 pm

3.4

−4.4

−5.9

−5.2

−6.5

−7.3

−3.5

4 pm

2.9

−3.7

−5.4

−6.1

−5.4

−7.1

−3.9

5 pm

2.4

−2.5

−4.0

−4.7

−3.4

−5.2

−2.7

(Time period – 10 May to 6 June)

Finding time to rest was found to be a challenge for women having responsibilities at home and in the fields. Those who work on their own land stated that they rest for an hour or more at home during the afternoons. People prefer to sit outdoors under the shade of trees. It was also observed that some house, especially ones which did not have trees around, have erected a thatched roof propped up by sticks outside the front of the house. Traditional cots were also kept outside the houses in the shade during summer months. Some shared that they rest for a day or two when they are affected by HRS.

About 25.6% households reported no specific measures being used. Almost all families reported increased consumption of water for drinking during summer. Almost 60% of families change their cooking schedules, with most of them (49.8%) cooking earlier in the day during summer months. In majority of households, male and female members slept outdoors and changed their sleeping pattern (waking up earlier in summers) (Photo 3).
Photo 3

Elderly woman resting outside her house during a summer afternoon. (Source: WOTR)

Respondents reported drinking more water. Other simple strategies such as seeking shade, staying indoors, wearing protective clothing, changing work schedule, and avoiding outdoor activity received less affirmative responses (performed only sometimes), which indicates limited scope to adopt such measures (Graph 4). Almost 89% and 79% respondents reported that the water bodies and tree cover in their respective villages were poor or very poor.
Graph 4

Coping strategies employed by respondents during very hot weather (In %, N = 215)

A large proportion of households (44%) approached private doctors for HRS. The average distance between villages and their preferred healthcare facilities was 9.5 km. Most people reported that the doctor they consulted was qualified (94.6%) (though this did not adequately corroborate with our earlier qualitative enquiries with local health practitioners). While individuals provided various reasons for inconvenience of visiting healthcare providers, poor transport facilities (reported by 65.7% households) was the most important, followed by distance to health center (41.4%) and cost of utilizing healthcare (31.4%).

About 53% of the study households reported having received information on heat stress from at least one reliable source like TV, radio, newspaper, or a medical professional. Majority of respondents (72% of sample households) felt it was important to have access to weather information. About 23% households received information only through word of mouth (Table 9). Other sources included information from friends, TV, family members, and doctors.
Table 9

Reported sources for information on heat stress

Type of source

Number

Percentage (N = 215)

At least one reliable source of information (TV, radio, newspaper, medical professional)

114

53.0

Hearsay

49

22.8

Didn’t hear any information

52

24.2

In recent years, heat action plans have been prepared at state level (e.g., Uttar Pradesh, Andhra Pradesh, Orissa, and Telengana), city level (e.g., Ahmedabad, Nagpur), and district level (e.g., Hazaribaug in Jharkhand state). Broadly, these action plans aim to build public awareness and community outreach, develop early warning system and institutional mechanism for inter-agency cooperation, build capacity for healthcare professionals, and promote adaptive measures. Their effectiveness on the ground needs to be assessed.

Conclusion

Heat-related symptoms were highly prevalent in the area. The common HRS were found to be headache, heavy sweating, and fatigue, which are typically of mild or moderate nature. It could be useful to use HRS as indicators of heat stress in an area to intervene before they become severe. However, it is important to note that these symptoms are not specific to heat stress, and so there is scope for underreporting and over-reporting. Though heat was considered a problem locally, its priority was relatively low for local people (Mhaskar et al. 2016).

Findings indicate that age, gender, wealth, and pre-existing health conditions were significantly associated with occurrence of HRS. Working men and women (31–59-year category) had the highest proportion of affected individuals compared to all other age groups. Though a greater proportion of men were affected as compared to women, the dimensions of vulnerability and exposure need further study to understand the mechanisms through which more men were found to be affected. Local people, in our qualitative study, attributed the perceived poorer health resilience in the current generation to change in diets over the years (Mhaskar et al. 2016).

The identified exposure factors of working outdoors during midday, roofing material of the households, and indoor ventilation were significantly associated with occurrence of HRS. A smaller proportion of women reported experiencing HRS as compared to men. The type of livelihoods and housing structures influenced exposure to heat stress. It was found that individuals performing physically intensive tasks were more vulnerable to heat stress and so were individuals residing in tin-roofed houses. Indoor temperature in tin-roofed houses was found to be generally high as compared to other roofing types – cement or tiled. During the peak heat hours, temperatures inside tin-roofed houses were more than the outdoor temperatures. This has implications for elderly and young children who may rest inside these houses during those times. The use of coolers reduced indoor temperatures, but the availability of electricity and water as well as the funds to purchase coolers is a challenge in rural areas. A very small proportion of houses (<4%) had these facilities. In addition, relatively high nighttime temperatures inside tin- and cement-roofed houses are of health concern for persons resting indoors.

Further in-depth studies are needed to monitor the indoor temperature of various housing structures (considering not only roofing but also ventilation, location of windows, type of walls, and flooring) and the effect on individuals. This would provide insights for improving the housing designs that can better handle peak heat conditions. There is a need for policy studies as well, as during the time of the study subsidies were being provided for purchase of tin roofs for houses. There is a need for health assessment before the launching of health-sensitive policies.

Existing coping strategies were found to be inadequate to protect people from indoor and outdoor heat-related stresses. Changing the work timing and reducing work on hot days was a challenge for many laborers working in other people’s farms. Those from poor families and woman-headed households also reported having to work without rest and even during periods of illness. Sleeping outdoors at night was also relatively common, despite other reported health risks such as mosquitoes (Mhaskar et al. 2016). A major challenge in rural areas in the context of heat stress is accessibility to well-equipped health centers. Though accessing healthcare was found to be a relatively common coping strategy, poorer families found healthcare to be expensive and avoided accessing it (Mhaskar et al. 2016). A more long-term strategy for competence on HRS, infrastructure, and accessibility to timely medical facilities is needed. For this, investments in upgrading of rural health infrastructure to handle heat stress-related incidences should be considered in all areas with high temperatures. In addition, there is a role for improved awareness on heat stress and associated precautionary measures.

In the future, population exposed to heat waves is projected to increase. Hence, there is a need for pre-emptive strategies to ensure that people in areas where heat waves are not yet a phenomenon are adequately supported to reduce their vulnerability. Improving health systems will benefit not just in the context of heat-related illnesses but for all illnesses, and so it is a no-regret intervention. Heat stress symptoms are easily recognized and can be used for early identification and prevention of more serious impacts such as heat stroke. Such measures will help protect individuals as well as the livelihoods. Effective planning through development of surveillance mechanism to monitor heat-related mortalities and morbidity could help in mitigating and avoiding heat-related stresses and deaths in the future. Housing designs should be improved to facilitate adequate ventilation and reduce the adverse impacts of tin roofs. The government can play an effective role here through existing housing schemes. Our study aims at understanding vulnerability within few villages in a semiarid area. It may be useful to compare our findings to villages in other agroclimatic regions to get a better understanding of relative vulnerability. However, different areas have different temperature-health response relationships, which make it difficult to compare.

At present there are heat action plans for some states (Government of Andhra Pradesh 2016; Odisha State Disaster Management Authority 2018) and for few cities (Ahmedabad Municipal Corporation et al. 2017; District Disaster Management Authority, Hazaribagh 2016). Maharashtra does not have a state-level heat action plan. Therefore, priority should be given to develop a comprehensive state-level heat action plan for Maharashtra, which addresses the needs and contexts of both urban and rural communities to prepare and prevent heat-related illness and deaths.

Cross-References

References

  1. Ahmedabad Municipal Corporation, Natural Resources Defense Council, Indian Metereological Department, Indian Institute of Public Health, Gandhinagar, Public Health Foundation of India, Mount Sinai School of Medicine, & Climate and Development Knowledge Network (2017) Ahmedabad heat action plan 2017 - Guide to extreme heat planning in Ahmedabad, India. India: NDRC. Retrieved from https://www.nrdc.org/sites/default/files/ahmedabad-heat-action-plan-2017.pdf
  2. Azhar GS, Mavalankar D, Nori-Sarma A, Rajiva A, Dutta P, Jaiswal A, Sheffield P, Knowlton K, Hess JJ (2014) Heat-related mortality in India: excess all-cause mortality associated with the 2010 Ahmedabad heat wave. PLoS One 9.  https://doi.org/10.1371/journal.pone.0091831
  3. Centers for Disease Control and Prevention (2011) Extreme heat and your health: warning signs and symptoms of heat illness [WWW document]. CDC Center for Disease Control Prevention. Available online at https://www.cdc.gov/disasters/extremeheat/warning.html
  4. Dash SK, Kjellstrom T (2011) Workplace heat stress in the context of rising temperature in India. Curr Sci 101:496–503Google Scholar
  5. Dholakia HH, Mishra V, Garg A (2015) Predicted increases in heat related morbidity under climate change in Urban India (No. W.P. No. 2015-05-02). Indian Institute of Management, Ahmedabad, IndiaGoogle Scholar
  6. Din A, Brotas L (2017) Assessment of climate change on UK dwelling indoor comfort. Energy Procedia 122:21–26.  https://doi.org/10.1016/j.egypro.2017.07.296. CISBAT 2017 International Conference Future Buildings & Districts – Energy Efficiency from Nano to Urban ScaleCrossRefGoogle Scholar
  7. District Disaster Management Authority, Hazaribagh (2016) Heat Wave Action Plan-2016 Hazaribagh. Hazaribagh, Jharkhand: District Disaster Management Authority. Retrieved from https://cdn.s3waas.gov.in/s3ed265bc903a5a097f61d3ec064d96d2e/uploads/2018/05/2018052643.pdf
  8. Gasparrini A, Guo Y, Hashizume M (2015) Mortality risk attributable to high and low ambient temperature: a multicountry observational study. Lancet 14:464–465.  https://doi.org/10.1016/S0140-6736(14)62114-0CrossRefGoogle Scholar
  9. Government of India (2018) Demography | Jalna [Internet]. Jalna. Available online at https://jalna.gov.in/about-district/demography/. Accessed 01 May 2018
  10. Government of Andhra Pradesh (2016) Heat wave action plan of Andhra Pradesh. Hyderabad, India: Government of Andhra Pradesh. Retrieved from www.imdhyderabad.gov.in/apsite/andhra.pdf.
  11. Harlan SL, Brazel AJ, Prashad L, Stefanov WL, Larsen L (2006) Neighborhood microclimates and vulnerability to heat stress. Soc Sci Med 63:2847–2863.  https://doi.org/10.1016/j.socscimed.2006.07.030CrossRefGoogle Scholar
  12. Ingole V, Rocklöv J, Juvekar S, Schumann B (2015) Impact of heat and cold on total and cause-specific mortality in Vadu HDSS – a rural setting in Western India. Int J Environ Res Public Health 12:15298–15308.  https://doi.org/10.3390/ijerph121214980CrossRefGoogle Scholar
  13. Intergovernmental Panel on Climate Change (2014) Human health: impacts, adaptation, and co-benefits. In: Climate change 2014: impacts, adaptation, and vulnerability. Part A: global and sectoral aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.  https://doi.org/10.1186/1471-2334-9-160
  14. Jackson LL, Rosenberg HR (2010) Preventing heat-related illness among agricultural workers. J Agromedicine 15:200–215.  https://doi.org/10.1080/1059924X.2010.487021CrossRefGoogle Scholar
  15. Khan F, Malik S, Rehman A (2014) Sheltering from a gathering storm: temperature resilience in Pakistan. ISET-International, BoulderGoogle Scholar
  16. Kjellstrom T, Holmer I, Lemke B (2009) Workplace heat stress, health and productivity-an increasing challenge for low and middle-income countries during climate change. Glob Health Action 2:1–6.  https://doi.org/10.3402/gha.v2i0.2047CrossRefGoogle Scholar
  17. Li M, Gu S, Bi P, Yang J, Liu Q (2015) Heat waves and morbidity: current knowledge and further direction-a comprehensive literature review. Int J Environ Res Public Health 12:5256–5283.  https://doi.org/10.3390/ijerph120505256CrossRefGoogle Scholar
  18. Lindemann U, Stotz A, Beyer N, Oksa J, Skelton D, Becker C, Rapp K, Klenk J (2017) Effect of indoor temperature on physical performance in older adults during days with normal temperature and heat waves. Int J Environ Res Public Health 14:186.  https://doi.org/10.3390/ijerph14020186CrossRefGoogle Scholar
  19. Lundgren K, Kuklane K, Gao C, Holmér I (2013) Effects of heat stress on working populations when facing climate change. Ind Health 51:3–15.  https://doi.org/10.2486/indhealth.2012-0089CrossRefGoogle Scholar
  20. McGeehin M, Mirabelli M (2001) The potential impacts of climate variability and change on temperature-related morbidity and mortality in the United States. Environ Health Perspect 109(Suppl):185–189.  https://doi.org/10.2307/3435008CrossRefGoogle Scholar
  21. Mhaskar B, Pradyumna A, Shinde Y, Kadam Y (2016) Heat stress and human health: vulnerability of rural communities in dry semi arid areas of India. Presented at the adaptation futures 2016, RotterdamGoogle Scholar
  22. Mueller V, Gray C, Kosec K (2014) Heat stress increases long-term human migration in rural Pakistan. Nat Clim Chang 4:182–185.  https://doi.org/10.1038/nclimate2103CrossRefGoogle Scholar
  23. Murari KK, Ghosh S, Patwardhan A, Daly E, Salvi K (2015) Intensification of future severe heat waves in India and their effect on heat stress and mortality. Reg Environ Chang 15:569–579.  https://doi.org/10.1007/s10113-014-0660-6CrossRefGoogle Scholar
  24. Nag PK, Nag A, Sekhar P, Pandit S (2009) Vulnerability to heat stress: scenario in Western India. National Institute of Occupational Health, AhmedabadGoogle Scholar
  25. National Disaster Management Authority (2016) Guidelines for preparation of action plan – prevention and management of heat-wave. Government of India, New Delhi. Available at: https://ndma.gov.in/images/guidelines/guidelines-heat-wave.pdf ; Accessed on 29.07.2018
  26. Odisha State Disaster Management Authority (OSDMA) (2018) Heat Action Plan for Odisha. Odisha, India: OSDMA. Retrieved from www.osdma.org/Download/heat-wave-action-plan.pdf
  27. Oudin Åström D, Bertil F, Joacim R (2011) Heat wave impact on morbidity and mortality in the elderly population: a review of recent studies. Maturitas 69:99–105.  https://doi.org/10.1016/j.maturitas.2011.03.008CrossRefGoogle Scholar
  28. Paul S, Bhatia V (2016) Heat stroke – emerging as one of the biggest natural calamity in India. Int J Med Res Prof 2:15–20Google Scholar
  29. Ponni M, Baskar B (2015) A study on indoor temperature and comfort temperature. Int J Eng Sci Invent 4:7–14Google Scholar
  30. Quinn A, Tamerius JD, Perzanowski M, Jacobson JS, Goldstein I, Acosta L, Shaman J (2014) Predicting indoor heat exposure risk during extreme heat events. Sci Total Environ 490:686–693.  https://doi.org/10.1016/j.scitotenv.2014.05.039CrossRefGoogle Scholar
  31. Sahu S, Sett M, Kjellstrom T (2013) Heat exposure, cardiovascular stress and work productivity in rice harvesters in India: implications for a climate change future. Ind Health 51:424–431.  https://doi.org/10.2486/indhealth.2013-0006CrossRefGoogle Scholar
  32. Tawatsupa B, Lim L-Y, Kjellstrom T, Seubsman S, Sleigh A (2010) The association between overall health, psychological distress, and occupational heat stress among a large national cohort of 40,913 Thai workers. Glob Health Action 3:5034.  https://doi.org/10.3402/gha.v3i0.5034CrossRefGoogle Scholar
  33. Tran KV, Azhar GS, Nair R, Knowlton K, Jaiswal A, Sheffield P, Mavalankar D, Hess J (2013) A cross-sectional, randomized cluster sample survey of household vulnerability to extreme heat among slum dwellers in Ahmedabad, India. Int J Environ Res Public Health 10:2515–2543.  https://doi.org/10.3390/ijerph10062515CrossRefGoogle Scholar
  34. Venugopal V, Chinnadurai JS, Lucas RAI, Kjellstrom T (2015) Occupational heat stress profiles in selected workplaces in India. Int J Environ Res Public Health 13:1–13.  https://doi.org/10.3390/ijerph13010089CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Adithya Pradyumna
    • 1
    • 2
    Email author
  • Ramkumar Bendapudi
    • 1
  • Dipak Zade
    • 1
  • Marcella D’Souza
    • 1
  • Premsagar Tasgaonkar
    • 1
  1. 1.Watershed Organisation Trust (WOTR)PuneIndia
  2. 2.Society for Community Health Awareness Research and Action (SOCHARA)BangaloreIndia

Personalised recommendations