Indian Journal of Public Health

: 2022  |  Volume : 66  |  Issue : 1  |  Page : 20--26

Climate and Disease vulnerability analysis in blocks of Kalahandi District of Odisha, India

Martand Mani Mishra1, Netrananda Sahu2,  
1 Research Scholar, Department of Geography, Delhi School of Economics, University of Delhi, Delhi, India
2 Assistant Professor, Department of Geography, Delhi School of Economics, University of Delhi, Delhi, India

Correspondence Address:
Netrananda Sahu
R. No. 1, Department of Geography, Delhi School of Economics, University of Delhi, Delhi - 110 007


Background: Diarrhea and typhoid, ancient water-borne diseases which are highly connected to rainfall are serious public health challenges in the blocks of Kalahandi district of Odisha, India. Objectives: Corroboration of rainfall and waterborne diseases are available in abundance; therefore, the objective of this article is to calculate the climate and disease vulnerability index (CDVI) value for each block of Kalahandi. Methods: We have applied the livelihood vulnerability index with some modifications and classify the three major categories, i.e., exposure, sensitivity, and adaptive capacity into six subcategories. These six subcategories are further divided into 26 vulnerability indicators based on a detailed literature review. Results: The result indicated that the Thuamul Rampur block, the southernmost part of the district is highly exposed to the annual and seasonal mean rainfall, and the Madanpur Rampur block lies in the northernmost part of the district is highly exposed to diarrhea and typhoid. Based on the calculation of the final CDVI value, nearly 50% of blocks of the Kalahandi district fall in the category of very high to high vulnerable zones. Furthermore, it has been observed that factors such as rainfall and disease distribution, vulnerable population and infrastructure, and education and health-care capacities had a notable influence on vulnerability. Conclusion: It is rare to find a health vulnerability-related study in India at this microlevel based on the suitable indicators selected for a tribal and backward region.

How to cite this article:
Mishra MM, Sahu N. Climate and Disease vulnerability analysis in blocks of Kalahandi District of Odisha, India.Indian J Public Health 2022;66:20-26

How to cite this URL:
Mishra MM, Sahu N. Climate and Disease vulnerability analysis in blocks of Kalahandi District of Odisha, India. Indian J Public Health [serial online] 2022 [cited 2022 Jun 26 ];66:20-26
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Variability in climatic factors such as rainfall, temperature, and humidity has a serious impact on human health and it's not a myth.[1],[2],[3] Changing climatic conditions and increasing the burden of diseases have become a major cause of concern in India. Ramification and association between rainfall and waterborne infectious diseases have been widely reported in several epidemiological research conducted worldwide.[4],[5],[6],[7] The impact of climatic factor-like rainfall on the diseases increases at the geometric ratio in tribal and backward regions due to the several contributing factors which include their demographic pattern, social setup, and economic condition. Rainfall variability and especially extreme rainfall may increase the burden of waterborne diseases. In addition to the exposure to rainfall and waterborne diseases, the combination of other factors such as vulnerable population, insufficient infrastructure, education, and unavailability of health-care capacity also play an important role in exacerbating vulnerability, particularly in socioeconomically backward tribal regions. Although several pioneer works have been done to understand the exposure and relationship between waterborne diseases and rainfall,[8] it is very difficult to find a systematic study on a tribal region that includes factors of sensitivity and adaptive capacity.

Kalahandi district is one of the most socioeconomically backward and vulnerable districts of India known for its poverty, disease, and malnutrition.[9] This district is located in the state of Odisha situated in the Eastern part of the country. Kalahandi is poorly affected by the diseases and is popularly known as the symbol of death and starvation in the country.[9]

The district is dominated by the tribal population that contributes nearly 29% of the total population. Due to its interior location, the population residing in different blocks of the district does not have access to the necessary amenities. Health-care disparities exist among the blocks of the district. It has also been observed from the secondary sources of data that patterns of rainfall and waterborne diseases are not similar among the blocks. We have included rainfall as a climatic factor because other major disease deciding climatic factor that is temperature has relatively little all over the district. Considerable variation in the demographic, educational, and health-care infrastructural facilities is found at the block level. Therefore, in this research work, we have considered 13 blocks of the Kalahandi district separately to understand their vulnerability related to their exposure to waterborne diseases and rainfall.

Vulnerability assessment includes three components: exposure, sensitivity, and adaptive capacity.[10],[11] In the exposure section, we have taken two components: rainfall and waterborne disease. In the district, there exist two major waterborne diseases, acute diarrheal disease and typhoid. Diarrhea contributes significantly to the highest morbidity and mortality among children younger than 5 years in age.[12] The present study focuses on the calculation of the vulnerability index (VI) value separately for each block. We have selected several indicators from the environment, disease, demography, and infrastructure sector and classify them into exposure, sensitivity, and adaptivity. These indicators have been chosen based on a detailed literature review.[13],[14],[15] Our approach of the study focuses on understanding the vulnerability of each block which will help in disease management, reducing the poverty of the population, and will lead to the development of health-care infrastructure whereever needed.

 Materials and Methods

Study area

Kalahandi district of Odisha is located in the southwestern portion of the state and covers a geographical area of 7920 km2. [Figure 1] Administratively, Kalahandi district is divided into two subdivisions, i.e., Bhawanipatna and Dharmagarh. These two subdivisions are further divided into seven and six blocks, respectively. There is vast variation among the blocks in terms of demographical characteristics, geographical settings, and infrastructural development.{Figure 1}

Physiography of the district comprises plain lands, hills, and mountains. The district receives maximum rainfall during the Indian Summer Monsoon Rainfall (ISMR) season. The temperature remains almost similar for all the blocks ranges between 4°C during the winter months and 45°C during the peak summer season. The district is majorly composed of the marginalized population includes Scheduled Caste (SC) 18.1% and Scheduled Tribe (ST) 28.5%.

Data collection

For calculation of the climate and disease VI (CDVI), we categorized different indicators into three broad contributes significantly of exposure, sensitivity, and adaptivity as mentioned in the IPCC concept. This study was conducted on the data collected from secondary sources that include reports from the state and central government reports.

In the exposure section, we have taken waterborne diseases data (2015–2018) and daily rainfall data (1988–2018) for each block. Data on the waterborne diseases (diarrhea and typhoid) was collected from the Integrated Disease Surveillance Programme, District Surveillance Unit, Kalahandi, and the Government of Odisha. Data on daily rainfall for 31 years in each block were obtained from the Odisha rainfall monitoring system, the Government of India. Data on the indicators of sensitivity were extracted from the census report (2011) and District Census Handbook (2015). The data were collected from multiple district-level agencies including i.e., district rural development agency, district planning and monitoring unit, rural water supply and sanitation department, and district social welfare department. Data on the adaptive capacity were extracted from the block development officer/executive officer report, chief medical officer, inspector office (Homeopathic and Ayurveda) Kalahandi report provided in the District Census Handbook.

Calculation of climate and disease vulnerability index

Following the steps of the livelihood VI (LVI) method,[16] we developed CDVI to measure the vulnerability of the blocks to rainfall and waterborne diseases. We reviewed the work done in the field of health and climate vulnerability analysis and made some modifications in the LVI to construct our VI. CDVI analysis applies a weighted average approach. The stepwise calculation of the CDVI has been summarized below.

Steps to calculate the climate and disease vulnerability index based on the livelihood vulnerability index formula

Step 1: Indicators

Values for all the indicators are to be standardized for all the blocks.

Step 1:

The steps can be broadly summarized as:


where Ix = Standardized value for the indicator.

Id = Value for the Indicator I for a particular block, d.

I (min) = Minimum value for the indicator across all the blocks.

I (max) = Maximum value for the indicator across all the blocks.

Step 2: Profiles

Indicator index values are combined to get the values for the profiles:


where n – number of indicators in the profile.

Indicator index i – Index of the ith indicator.

Step 3: Components

Once values for each profile were calculated, they were averaged to obtain the VI for each component.


where Wpi is the weightage of the Profile i.

WPi is the number of indicators that make up each profile and are included to ensure that all.

Indicators contribute equally to the overall VI.

Calculate the value for each block is then categorized into the low, medium, and high categories.

Step 4: Vulnerability index

VI = (exposure ‒ adaptive capacity) × sensitivity

The final calculated value of the VI for each block is categorized between − 1 (least vulnerable) and 1 (most vulnerable).


In this study, we have combined both the climatic (rainfall during the past 31 years) and disease factor (waterborne disease) to calculate the level of exposure of each block separately [Table 1]. For interpreting the pattern of rainfall in each block, we calculated the annual and seasonal (ISMR) average rainfall received daily for the past 31 years [Figure 2]a and [Figure 2]b. It has been observed that annual average rainfall is found to be moderate to high in the northern, northeastern, and southern regions of the district in comparison to the Western and centrally located blocks [Figure 2]a. When we observe the rainfall in the ISMR (June, July, August, and September), it is quite different from the annual average [Figure 2]b. Low levels of rainfall were observed in the central, east, and west lying blocks, whereas it is moderate to high for the northern and southern blocks [Figure 2]b. The percentage of diarrhea cases shows the pattern that blocks lying in the southwestern part of the district (Dharmagarh, Kokasara, and Jaipatna) and the northern region (Narla) are less exposed in comparison to the other blocks of the district [Figure 2]c. Typhoid has its maximum impact in the northern region of the district which includes blocks such as Madanpur Rampur, Narla, Bhawanipatna, and Karlamunda [Figure 2]d. The combined calculated value of the exposure section shows that blocks lying in the extreme North (Madanpur Rampur) and South (Thuamul Rampur) are highly exposed to these two factors. Blocks of the district, namely Karlamunda, Kesinga, Narla, Golamunda, Bhawanipatna, Lanjigarh, Junagarh, Koksara, Kalampur, and Jaipatna fall under the category of moderately exposed blocks [Figure 3]a. Dharmagarh block situated in the western zone of the district is the least exposed.{Table 1}{Figure 2}{Figure 3}

In the sensitivity section, we have taken population and infrastructure vulnerability defining indicators into consideration to understand the level of sensitivity. We have taken the data of malnourished children between the age group of 0–3 and 3–6 years as an indicator in this study. These malnourished children are highly vulnerable to water-borne diseases such as diarrhea and typhoid, and therefore, will be of a high level of importance while calculating the vulnerability. People belonging to the marginalized class (SC and ST) are also vulnerable because of their low level of literacy, poor economic condition, and living environment. [Table 1] Poverty plays an important role in increasing mortality related to any disease that is why we have also considered the percentage of the population living below the poverty line in each block of the district as an indicator [Table 1]. Different indicators from the infrastructural availability have also been taken to study the sensitivity of each block [Table 1]. The calculated value of sensitivity shows that blocks lying in the southern part, i.e., Kokasara, Jaiyapatna, and Thuamul Rampur, are highly sensitive [Figure 3]b. Blocks in the eastern (Golamunda), western (Lanjigarh), and northern part (Karlamunda) are categorized in moderately sensitive zones. It is important to mention here that the highly exposed block such as Madanpur Rampur along with Narla, Kesinga, Bhawanipatna, Dharamgarh, Junagarh, and Kalampur are categorized as the least sensitive blocks of the district [Figure 3]b. Indicators from education and health care sectors were considered as a basis to understand the level of adaptive capacity of each block [Table 1]. The adaptive capacity map shows that blocks, namely, Madanpur Rampur and Lanjigarh are found to be in the very high capacity zone [Figure 3]c. The calculated adaptive capacity value is very less for the Golamunda, Junagarh, and Jaiyapatna blocks of the district which contributes positively to increasing their vulnerability factor [Figure 3]c.

Finally, we have applied the formula of VI ([exposure − adaptive capacity] × sensitivity) to calculate the final CDVI value for the blocks. The values of the CDVI are categorized into four classes between −1 and +1. The blocks near to the negative value are considered the least vulnerable blocks, whereas the blocks which are near to positive value and are positive are considered moderate, high, and very highly vulnerable blocks. The final composite value of CDVI ranges between −0.17 and +0.11. CDVI shows that the Thuamul Rampur block is the very highly vulnerable block of the district with the highest positive value [Figure 3]d. Blocks lying in the central region of the district (Golamunda, Bhawanipatna, Junagarh, and Kalampur) and the Northern part (Madanpur Rampur) are categorized as highly vulnerable blocks. Blocks in the Northern zone (Karlamunda, Kesinga, and Narla) and Southern zones (Jaipatna) fall in the moderately vulnerable zone [Figure 3]d. Lanjigarh and Kokasara blocks lying in the western and eastern parts of the district were categorized as the least vulnerable block of the district [Figure 3]d.


The relationship between rainfall and water-borne diseases indicates that excess rainfall increases the risk of water contamination,[18] which further leads to an increase in the number of cases in a particular region. Contaminated water plays an important role in increasing the burden of public health, particularly in the areas which are designated as a tribal and backward region. The nonavailability of a proper health-care system and inequality in quality health care do not let this district become prosperous. Nowadays, the focus of the researchers has been shifted on understanding the impact of climatic factors on human health (particularly on diseases) so that the research could be utilized for better policy formation.[19] Water-borne diseases such as diarrhea and typhoid are major contributors to morbidity and mortality in the Kalahandi district of Odisha. By understanding the pattern of rainfall and waterborne diseases, we can identify the blocks which are highly exposed to both these factors. It is a well-established fact that rainfall is highly correlated with waterborne diseases,[20],[21],[22] which are also reflected in our study.

It is interesting to note that Thuamul Rampur is highly exposed to both the factors rainfall (annual and ISMR season) and Mandanpur Rampur is to the disease factors but Thuamul falls in the category of a very high vulnerable zone, whereas Madanpur lies in the zone of high vulnerability. The role of sensitivity and adaptive capacity is very crucial. In the sensitivity section, we observed that in all four indicators, Thuamul Rampur has scored the highest value. This block is mainly inhabited by the SC and ST population most of whom in extremely poor conditions. Children in the age group of 0–3 and 3–6 years are highly malnourished which places this block in the category of the very highly vulnerable block. Other factors such as low level of literacy rate, graduate's percentage, and nonavailability of proper health-care infrastructure are also major contributing factors. Although the Madanpur Rampur block is highly exposed to both the diseases and moderately exposed to the rainfall factor, it is not in the category of the very highly vulnerable block due to its better score in the sensitivity and adaptive capacity section. Bhawanipatna the district headquarter is moderately exposed to all the four factors in the exposure section but lies in the category of the highly vulnerable block due to its moderate level of adaptive capacity. Golamunda, Junagarh, and Jaipatna block have also scored very poorly in the adaptive capacity section which places them along with Madanpur Rampur and Bhawanipatna. Dharmagarh block is neither exposed to rainfall and not to diseases. Because of the combining factors of all three components, Dharmagarh is categorized as the least vulnerable block of the district along with Kokasara and Lanjigarh. Sensitivity is very high in the Kokasara block, but its moderate exposure and good adaptive capacity placed it in the least vulnerable category. Lanjigarh block is mostly inhabited by the tribal population, but it is placed in the low vulnerable category due to better availability of health infrastructure and its moderate exposure and sensitivity.


It is mentioned earlier that it is rare to find a health study on such a micro-level. Among various backward regions, Kalahandi in particular has been a serious concern for the government, scientists, and researchers. The study seeks to capture the health vulnerabilities of the district concerning disease and rainfall patterns. Understanding these factors and their correlation with the demographic, economic, and infrastructure will surely help the policy-makers to understand the present situation of the block and find a better solution through proper policy planning and implementation. The limitation of the study is that we also wanted to include vector-borne disease data which is unfortunately not available for the block level for consecutive years at this microlevel.


We gratefully acknowledge the support provided by the Government of Odisha and Kalahandi district health department officials for providing data on rainfall and diseases incidences. We are also thankful to the Department of Geography, Delhi School of Economics, the University of Delhi for providing access to ArcGIS Version 10.2 and SPSS software version 22.0 that is used to draw the map and do the statistical calculations.

Financial support and sponsorship


Conflicts of interest

There are no conflicts of interest.


1Wu J, Yunus M, Streatfield PK, Emch M. Association of climate variability and childhood diarrhoeal disease in rural Bangladesh, 2000-2006. Epidemiol Infect 2014;142:1859-68.
2Amuakwa-Mensah F, Marbuah G, Mubanga M. Climate variability and infectious diseases nexus: Evidence from Sweden. Infect Dis Model 2017;2:203-17.
3Field CB, Barros VR, Dokken DJ, Mach KJ, Mastrandrea MD, Bilir TE, et al. Climate Change 2014 Impacts, Adaptation, and Vulnerability Part A: Global and Sectoral Aspects Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge: Cambridge University Press; 2014. p. 1-32.
4Rose JB, Daeschner S, Easterling DR, Curriero FC, Lele S, Patz JA. Climate and waterborne disease outbreaks. J Am Water Works Assoc 2000;92:77-87.
5Jofre J, Blanch AR, Lucena F. Water-Borne Infectious Disease Outbreaks Associated with Water Scarcity and Rainfall Events. Berlin, Heidelberg: Springer; 2009. p. 147-59.
6Charron D, Thomas M, Waltner-Toews D, Aramini J, Edge T, Kent R, et al. Vulnerability of waterborne diseases to climate change in Canada: A review. J Toxicol Environ Health A 2004;67:1667-77.
7Carlton EJ, Eisenberg JN, Goldstick J, Cevallos W, Trostle J, Levy K. Heavy rainfall events and diarrhea incidence: The role of social and environmental factors. Am J Epidemiol 2014;179:344-52.
8Chowdhury FR, Ibrahim QS, Bari MS, Alam MM, Dunachie SJ, Rodriguez-Morales AJ, et al. The association between temperature, rainfall and humidity with common climate-sensitive infectious diseases in Bangladesh. PLoS One 2018;13:e0199579.
9Mishra MM, Sahu N, Pandey BW, Singh RB. Assessing human health vulnerabilities in the blocks of Kalahandi district of Odisha, India. Disaster Adv 2021;14:32-40.
10Binita KC, Shepherd JM, Gaither CJ. Climate change vulnerability assessment in Georgia. Appl Geogr 2015;62:62-74.
11Weis SW, Agostini VN, Roth LM, Gilmer B, Schill SR, Knowles JE, et al. Assessing vulnerability: An integrated approach for mapping adaptive capacity, sensitivity, and exposure. Clim Change 2016;136:615-29.
12GBD 2017 Causes of Death Collaborators. Global, regional, and national age-sex-specific mortality for 282 causes of death in 195 countries and territories, 1980-2017: A systematic analysis for the Global Burden of Disease Study 2017. Lancet 2018;392:1736-88.
13Sujakhu NM, Ranjitkar S, He J, Schmidt-Vogt D, Su Y, Xu J. Assessing the livelihood vulnerability of rural indigenous households to climate changes in central Nepal, Himalaya. Sustainability 2019;11:1-18.
14Urothody AA, Larsen HO. Measuring climate change vulnerability: A comparison of two indexes. Banko Janakari 2010;20:9-16.
15Bae HJ, Kang JE, Lim YR. Assessing the health vulnerability caused by climate and air pollution in Korea using the fuzzy TOPSIS. Sustainability 2019;11:1-15.
16Hahn MB, Riederer AM, Foster SO. The livelihood vulnerability index: A pragmatic approach to assessing risks from climate variability and change – A case study in Mozambique. Glob Environ Change 2009;19:74-88.
17District Rural Development Agency Kalahandi, District statistical handbook, Kalahandi, 2011 & 2015.
18Dutta BP, Kumar N, Meshram KC, Yadav R, Sodha SV, Gupta S. Cholera outbreak associated with contaminated water sources in paddy fields, Mandla District, Madhya Pradesh, India. Indian J Public Health 2021;65:S46-50.
19Hess JJ, Eidson M, Tlumak JE, Raab KK, Luber G. An evidence-based public health approach to climate change adaptation. Environ Health Perspect 2014;122:1177-86.
20Thomas KM, Charron DF, Waltner-Toews D, Schuster C, Maarouf AR, Holt JD. A role of high impact weather events in waterborne disease outbreaks in Canada, 1975 – 2001. Int J Environ Health Res 2006;16:167-80.
21Curriero FC, Patz JA, Rose JB, Lele S. The association between extreme precipitation and waterborne disease outbreaks in the United States, 1948-1994. Am J Public Health 2001;91:1194-9.
22Auld H, MacIver D, Klaassen J. Heavy rainfall and waterborne diseases outbreaks: The Walkerton example. J Toxicol Environ Heal Part A 2004;67:1879-87.