J. Sci. Technol. Environ. Inform. | Volume 12, Issue 02, 806-820 | https://doi.org/10.18801/jstei.120223.81.
Article type: Research article, Article received: 15.06.2023; Revised: 28.07.2023; First published online: 30 August, 2023.
Article type: Research article, Article received: 15.06.2023; Revised: 28.07.2023; First published online: 30 August, 2023.
Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh
Prome Debnath 1, Ayesha Siddiqua 2 and Avijit Biswas 3
1 Department of Disaster Risk Management, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh.
2 Department of Disaster Resilience and Engineering, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh.
3 Dept. of Agriculture, Bangabandhu Seikh Mujibar Rahman Science and Technology University, Gopalganj, Bangladesh.
✉ Corresponding author: [email protected].
1 Department of Disaster Risk Management, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh.
2 Department of Disaster Resilience and Engineering, Patuakhali Science and Technology University, Dumki, Patuakhali-8602, Bangladesh.
3 Dept. of Agriculture, Bangabandhu Seikh Mujibar Rahman Science and Technology University, Gopalganj, Bangladesh.
✉ Corresponding author: [email protected].
Abstract
The coastal region of Bangladesh is highly vulnerable to climate induced disasters, so the study was carried out at Amtali union, Bangladesh. The aim of this study was to find out the existing health vulnerability and the perceived health vulnerability that will arise due to further disaster situations and investigate the relationship between respondents’ socioeconomic characteristics with actual health vulnerability and perceived health vulnerability. The information was collected through face-to-face questionnaire survey and key informant interviews. To find out the health vulnerability of this area, the author used vulnerability index and perceived health vulnerability index that was measured through calculating indicators accumulative score divided by the obtainable score. After that, Coefficients of Correlation was used to find out the relationship between socioeconomic characteristics of respondent and actual vulnerability vs perceived health vulnerability. In the study area maximum respondents were male and middle aged with no literacy and low-level monthly income rate. The study revealed that according to respondents’ opinion, the value of existing health vulnerability was 0.48, less than the perceived health vulnerability 0.67. The respondents of study area thought that their health conditions are deteriorating day by day for facing different kinds of disaster within a short period. So, it enhanced their unstable rate which may turn towards health risks for future adverse situations and made them more vulnerable to those disasters. Correlation analysis indicated that education (-0.268”), income (-0.312”) and gender (-0.158’) had a negative significant relationship with existing health vulnerability and only the marital status (0.211”) had positive relationship with actual health vulnerability of study area. On the other hand, for perceived health vulnerability cases, education (0.346”) and marital status (0.357”) had positive relationship and age (-0.320”), income (-0.461”) had negative relationship for triggering the health condition of victim people. Based on this analysis, in the study area, academic education and knowledge of disaster risk reduction and disaster risk management are the main focus so that they can extend their livelihood options to keep their income level stable and develop adaptability against any negative climatic change to disaster.
Key Words: Health, Vulnerability, Perceive Health Vulnerability, Climate Change and Disaster
The coastal region of Bangladesh is highly vulnerable to climate induced disasters, so the study was carried out at Amtali union, Bangladesh. The aim of this study was to find out the existing health vulnerability and the perceived health vulnerability that will arise due to further disaster situations and investigate the relationship between respondents’ socioeconomic characteristics with actual health vulnerability and perceived health vulnerability. The information was collected through face-to-face questionnaire survey and key informant interviews. To find out the health vulnerability of this area, the author used vulnerability index and perceived health vulnerability index that was measured through calculating indicators accumulative score divided by the obtainable score. After that, Coefficients of Correlation was used to find out the relationship between socioeconomic characteristics of respondent and actual vulnerability vs perceived health vulnerability. In the study area maximum respondents were male and middle aged with no literacy and low-level monthly income rate. The study revealed that according to respondents’ opinion, the value of existing health vulnerability was 0.48, less than the perceived health vulnerability 0.67. The respondents of study area thought that their health conditions are deteriorating day by day for facing different kinds of disaster within a short period. So, it enhanced their unstable rate which may turn towards health risks for future adverse situations and made them more vulnerable to those disasters. Correlation analysis indicated that education (-0.268”), income (-0.312”) and gender (-0.158’) had a negative significant relationship with existing health vulnerability and only the marital status (0.211”) had positive relationship with actual health vulnerability of study area. On the other hand, for perceived health vulnerability cases, education (0.346”) and marital status (0.357”) had positive relationship and age (-0.320”), income (-0.461”) had negative relationship for triggering the health condition of victim people. Based on this analysis, in the study area, academic education and knowledge of disaster risk reduction and disaster risk management are the main focus so that they can extend their livelihood options to keep their income level stable and develop adaptability against any negative climatic change to disaster.
Key Words: Health, Vulnerability, Perceive Health Vulnerability, Climate Change and Disaster
Article Full-Text PDF
| 81.12.02.23_actual_health_vulnerability_vs_perceived_health_vulnerability_due_to_disaster_at_amtali_union_bangladesh.pdf |
|
Share This Article
|
|
I. Introduction
Climate change has been regarded as the largest global health challenge in the 21st century. Bangladesh is regarded as one of the nation’s most severely impacted by climate-related calamities worldwide. This country is physically vulnerable to climate change due to its unfavourable geographic location and low, flat topography (Rahman, 2017). In addition, several socioeconomic factors, including high population density, extreme poverty, gender inequality, reliance on climate-vulnerable livelihoods sectors, particularly agriculture, fishing, and hunting and gathering, institutional insufficiency, and a lack of adaptation funds, are making people more susceptible to harm (Khan et al., 2011). The effects of climate change are complex, dynamic, and more acutely felt in underdeveloped countries and communities, which might influence people's health and natural systems. Due to its fragile geographic location, Bangladesh, one of the South Asian nations, has begun to experience significant public health effects of climate change (Hasib and Chathoth, 2016). The Interco relations between the potential consequences of a changing climate; the communities are exposed to both positive and negative variables. These include sensitivity to increasing prevalence of health concerns and the capacity to plan for responding to these threats (Ebi and Bowen, 2016). The biggest danger to public health in the twenty-first century was climate change, linked to environmental degradation and harmful health effects (Patrice et al., 2021). Changes in freshwater resources, food availability, and access to proper healthcare facilities are the climate change effects on human health that are most concerning (Few, 2007). Vulnerability is considered from the viewpoint of the health sector as the totality of all risk and protective variables that determine whether a person or group suffers negative health effects from exposure to an extreme event (Balbus and Malina, 2009). Four factors must be geographically integrated to measure the health susceptibility to climate change: the actual climatic change, any future ecological change, the presence of human disease, and human habitation (Sari et al., 2003).
The IPCC's predicted changes to the global climate might indirectly or directly impact human health (Kim et al., 2014). Infectious disease outbreaks and transmission, thermal stress-related mortality from extremely high temperatures, and mortality and morbidity from extreme weather events like floods and storms are some of the direct effects on health. On the other hand, reproduction and spread of microorganisms and vectors, as well as harsh climatic conditions, indirectly impact health. The most vulnerable populations in affluent nations as well as low income countries, where the ability for adaptation is weakest, are anticipated to be affected by the majority of the negative consequences of climate change on health (Haines et al., 2006). Over 70% of respondents in the United States thought their jurisdiction (a county or city) would face severe adverse health consequences related to climate change during the following two decades (Akerlof et al., 2010). During severe occurrences, the risk of negative health outcomes was raised for the underprivileged, pregnant women, children, those with chronic illnesses, and people with mobility or cognitive impairments (Balbus and Malina, 2009). Additionally, vulnerability is influenced by the social determinants of health, including social capability, social norms, culture, healthcare services, access to and quality of education, transportation alternatives and most importantly, availability of resources (Braveman and Gottlieb, 2014). A disaster can have many different effects on a community, so the main goal of disaster management from a public health perspective is to evaluate the effectiveness of disaster relief programs, assess the needs of populations affected by disasters, match resources to those needs, prevent further negative health effects, implement disease control strategies for clearly defined problems, and improve contingency plans for various types of future disasters (Leppold et al., 2022). Although adaptation measures present practical challenges, especially in low-income nations, they should be prepared to mitigate some negative effects. In order to improve health, low-income countries should identify cost-effective intervention options in the health sector, such as early planning, which can help reduce future negative health impacts, and mitigation strategies, such as using a variety of renewable energy sources, which can both address climate change and improve health by reducing pollution. However, efficient emergency medical response depends on identifying these many medical and health issues before they occur and delivering the necessary interventions (relief supplies, equipment, and personnel) at the exact moments and locations where they are most required (Beyramijam et al., 2021).
The Bangladeshi government has acknowledged the difficulties in addressing the health-related risks brought on by the various disasters the nation has already experienced and has taken several good initiatives to reduce such risks. The 2008 Bangladesh Climate Change Strategy and Action Plan emphasized the necessity of putting in place surveillance systems for potential disease outbreaks, both old and new, and made sure health services are ready to handle future demands. Bangladesh is among the first nations to create a Climate Change and Health Promotion Unit (CCHPU) under the Ministry of Health and Family Welfare. The CCHPU's mandate is to conduct research, assess health promotion and climate change programs, and monitor such programs. To close a critical knowledge gap in the health field in the context of climate change, the Ministry of Environment has determined the necessity of conducting extensive statewide research focusing on climate-sensitive illnesses (Limin and Muthukumara, 2014). But even so, Disaster-related health concerns in Bangladesh are frequently underappreciated and understudied. Most of the time, when dealing with other disaster-related effects, this sector is given the least amount of attention. In addition, there is a significant lack of understanding among coastal residents regarding the health risks associated with disasters and risk control strategies. It is crucial to evaluate current vulnerabilities, understand how coastal residents are affected by health risks, and comprehend how the current healthcare system responds to their demands. The aim of this study to find out the existing and the perceived health vulnerability of the Amtoli union those are arisen due to disaster situations and after that, analyze the relationship between respondents’ socioeconomic characteristics with actual health vulnerability and perceived health vulnerability to highlight the triggering factor which may instigate the health vulnerability during disaster time towards the worst level.
II. Materials and Methods
Description of the study area
Barguna district lies on the bank of Payra river. This District has a total area of 1939.39 square kilometres. The area is located with latitude of 22.158°N and longitude of 90.1264°E. The study was conducted at Amtoli union of Amtoli upazila under Barguna district (Figure 01). Amtoli union has 50.59 square kilometres and literacy is 50.6%. Amtoli union was purposively selected for the study, as it is located near the Bay of Bengal and frequently affected by different kinds of disasters, especially water-related disasters. The average annual temperature was 26.0 °C and precipitation rate was 2516mm annually (BMD, 2015). Most common climatic events are floods, cyclones, heavy rainfall, river erosion, epidemic etc. CARE Bangladesh (2020) stated that Amtoli union was severely affected by the recent severe cyclone, known as super cyclone Amphan (2020). The lower part of Amtoli union was flooded, several houses, trees and schools collapsed due to strongest cyclonic storm. Some families were in waterlogged condition for several days due to high tide. It caused multiple health issues with massive damage and illness, potential disease outbreaks, disruption of healthcare facilities etc., along the study location. This was the reason for selecting the Amtoli Union for study area.
Figure 01. Study area map of Amtoli Union (see in pdf)
Data collecting instrument
A structured interview schedule was used as the data-gathering instrument. The interview schedule was carefully prepared, considering the objectives of the study. Qualitative methods such as Key informant interviews and focus group discussions were implied to get a clear picture of this situation.
Measurement of Variables
Measurement of independent variables
Sl. No Variable Measurement technique
1 Age Year
2 Education Illiterate=1, only sign=2, primary=3, secondary=4, higher secondary=5, graduate=6
3 Gender Male=1, Female=2
4 Marital status Single=1, Married=2, Divorced=3, Widow=4
5 Religion Muslim=1, Hindu=2, Christian=3, Buddhist=4
6. Income Taka
Measurement of Dependent variable
Actual Health Vulnerability Index: Actual health Vulnerability index was measured by computing an ‘obtain score’ and obtainable score based on total number of questionnaires. Here, used a formula to measure actual health vulnerability index.
AHVI= ∑ Score obtain per statement/Obtainable score
There were 26 items were taken, which were calculated in an excel sheet. Some codes were used to measure VI (Vulnerability Index). They were 0 and 1. Then calculated as the summation of score obtained per statement (26 items) divided by obtainable score (26 items). Then the result of vulnerability index was made (Table 01).
Table 01. Actual Health Vulnerability Index (see in pdf)
Perceived Health Vulnerability Index:
The household survey and KII questionnaires identified the perceived health vulnerability. The questionnaires collected future health problems, loss of life, physical capability, different contagious diseases, facilities of medical treatment, financial ability, Govt. health policies, etc. Here, used a formula to measure perceived health vulnerability index.
PHVI= ∑ Score obtain per statement/Obtainable score
There were 10 items taken and these items were calculated in excel sheet. Some codes were used to measure PRI (Perceived Risk Index). They were 0 and 1. Then calculated the summation of score obtained per statement (10 items) and this was divided by obtainable score (10 items). Then the result of perceived risk index was made (Table 02).
Table 02. Perceived Health Vulnerability (see in pdf)
Statistical tests
Firstly the data were inputted into a master sheet which was obtained from the respondents then compiled, tabulated and analyzed by the objectives of the study. Mean, percentage distribution and standard deviation those statistical methods were used in describing the dependent variables. Tables were used to presenting the data. Pearson‖s Product Moment Coefficients of correlation (r) analysis was used for exploring the relationship between the selected characteristics of the respondents with their actual health vulnerability and perceived health vulnerability due to climate change induced disaster.
III. Results and Discussion
Socio-economic profile of respondents
A total of 61 per cent of the survey respondents were male and 39 percent were female. Now in Bangladesh, the ratio of men and women is same, but still, the male dominated society doesn’t want to permit women for their freedom. Additionally, climate variability has pushed women into a vulnerable and marginalized position in Bangladesh. Especially natural disasters and man-made disasters like socially constructed systems had made the situation possible where climate change plays a key role in instigating the vulnerabilities of woman (Rahman, 2013).
In Table 03 It was found that 28.5 percent of respondents were category -2 aged, 37.5 percent were category -3 aged, and the rest 21.5 percent respondent’s age were upper 50 years. Most of the respondents in the study area age were between 36 to 50 or middle aged. Climate related injury and illness were mostly felt by children and elderly (UNICEF, 2021). Older people, especially those already in poor health, are particularly vulnerable to climate-related events and it will be severed over the next 20-30 years (Oven et al., 2012). Furthermore, the health of older adults can be disproportionately affected by environmental hazards associated with climate change and air pollution (e.g., increasing temperatures, extreme events and poor air quality (Benivolenj and Deregni, 2019)
Educational background of the farmers was classified into six categories: illiterate 51.5% can sign only 19%, primary level 3%, 10.5% of respondents have completed high school/secondary school, Higher secondary School percentage 11.5% and only 4.5% respondents appeared to university. Data showed that half of the respondent (51.5 percent) in this study area had no education. People with higher education levels were more likely to make efforts to health response, as they had higher perceived benefits and perceived severity when facing climate change (Li et al., 2021). Societies can improve the most effective long-term defense against the dangers by strengthening human capacity, primarily through education about risk perception, skills and knowledge, indirectly reducing poverty and improving health and resources (Muttarak and Lutz, 2014). Educational programs and policies are crucial public health interventions (Han and Truma, 2015).
In the study area, maximum respondents were married (80%). As the respondents were between 36 to 50 or middle-aged, it was natural that according to Bangladesh culture, middle-aged people must be married or widowed. On the other hand, most of the respondents in study area were Muslim (95.5%) (Table 03).
Table 03. Socioeconomic characteristics of respondent (see in pdf)
In analyses, stratified information given that the respondent’s monthly household income (BDT) was collected to know their socio-economic status. The people were categorized into three income groups and their response has been recorded: ‘low income’ (Taka 1200-7000 thousand), ‘medium income’ (Taka 7000-13000 thousands) and ‘high income’ (above Taka 13000 thousands). Highest portion of farmers (90.5 percent) were in low to medium income category. Due to low social and economic status, the respondents of study area could not properly fight against health problems raised from different kinds of adverse situations. People with low incomes could not invest large amounts of money in healthcare issues. The higher economic group was reported to have more resources to respond to climate change than the lower economic group (Li et al., 2021). Balasubramanian (2018) express that Climate change had not affected the world equally and people of lower socioeconomic status had experienced more negative effects than others. The overall balance of climate change is likely to affect the populations in low-income countries and particularly vulnerable to the adverse effects (Moreno, 2006; Hanies, 2006).
Actual health vulnerability index
Vulnerability is the fundamental underlying risk factor. It indicates the negative set of conditions of something or someone. From this analysis, it was shown that in this study area, people suffered from diseases due to disaster and following fewer shelter facilities, less medical facilities, no savings and unavailability of fresh water and hygienic toilet system were the most negative set of conditions that create them more vulnerable towards adverse situation (Table 04).
Table 04. Measurement of Actual Health Vulnerability of Study area’s People (see in pdf)
Perceived health vulnerability index
Perceived risk is a subjective judgment of an individual, an individual’s perceived susceptibility to a threat is a key component of many health behavior change theories (Ferrer and Klein, 2015). This study found out the study area’s people perception of their future disaster induced health vulnerability (Table 05). The result showed that people in study area thought there would be more chances of live loss due to disaster-induced health problems in the future. On the other hand, as per respondents’ perception, in future there will less financial vulnerability than present situation.
Table 05. Measurement of Perceived Health Vulnerability of Study area’s People (see in pdf)
Relationship between selected characteristics of the respondents and Actual Health Vulnerability
Pearson Product Moment co-efficient was computed to determine the extent of relationship between the dependent variable and independent variables (at 1% and 5% level of significance). The results of correlation analysis are shown in Table 06.
Table 06. Relation between the demographic characteristics of the respondents with their actual health vulnerability (see in pdf)
Respondent’s education and their actual health vulnerability: The co-efficient of correlation between education and their health vulnerabilities was negative and significant at the 1% probability level (r = -.268”). This indicates that level of vulnerabilities will increase with decreasing of educational level. Uneducated people did not know how to mitigate vulnerabilities to fight against disaster. So, if the percentage of educated people increases in the study area, they will be more functional before, during and after disaster.
Respondent’s marital status and their actual health vulnerabilities: The co-efficient correlation between marital status and their health vulnerabilities was positive and significant at the 1% probability level (r = .211”). This data indicates that vulnerabilities will increase if the respondent's marital status becomes single and married to divorced and widow. Widow and divorced women and men are more vulnerable because of absence of supporting person and the most considered factor is age issue.
Respondent’s income and their actual health vulnerabilities: The co-efficient correlation between income and their health vulnerabilities was negative and significant at the 1% probability level (r = -.312”). This indicated that vulnerabilities increase with decreasing in income level. Because if the affected area’s people had low-income level, then they have less capacity to fight against disaster and it will increase their vulnerabilities in an ongoing process.
Respondent’s gender and their actual health vulnerabilities: The co-efficient of correlation between gender and their health vulnerabilities was negative and significant at the 5% level of probability (r = -.158’). This negative relationship indicates that vulnerabilities will increase if the study area’s male population decreases or the female population increases. As women have less strength and most of them have no proper knowledge to reduce health risks arising from different kinds of adverse situations, they are more vulnerable than males.
Relationship between selected characteristics of the respondents and Perceived Health Vulnerability
Pearson Product Moment co-efficient was computed to find out the extent of relationship between the dependent variable and independent variables (at 1% and 5% level of significance). The results of correlation analysis are shown in Table 07.
Table 07. Relation between the demographic characteristics of the respondents and perceived health Vulnerability (see in pdf)
Respondent’s age and their perceived health Vulnerability: The co-efficient correlation between age and their perceived health vulnerability was negative and significant at the 1% probability level of (r = -.320”). It indicates that the perceived health risk will be increased if the age of respondents will decrease. Because most of the respondents in the study were middle-aged, they thought that old people had less health capacity to cope with this kind of adverse situation.
Respondent’s education and their perceived health Vulnerability: The co-efficient correlation between education and their perceived health vulnerability was positive and significant at the 1% probability level (r = .346”). It means that assessing the possibility of suffering negative health events will be increased with the increase of educational level of respondents. Because if the people in study area will literate, they will understand how to deal with an emergency and disaster arising from it.
Respondent’s marital status and their perceived health Vulnerability: The co-efficient of correlation between marital status and perceived health vulnerability was positive and significant at the 1% level of probability (r = .357”). According to the result, the perceived health risk of respondents will be increased with the change of respondent’s marital status, like from married to divorced or widow. Because most of the divorced and widow in the study area was woman and the vulnerability of woman is always greater than the man.
Respondent’s income and their perceived health Vulnerability: The co-efficient of correlation between income and their perceived health vulnerability was negative and significant at the 1% level of probability (r = -.461”). Decreasing the level of income as well as increasing the perceived health risks. Because if respondents cannot take proper treatment for their existing disabilities, then it will increase their vulnerability towards disaster or any kind of negative situation in the near future.
IV. Conclusion
The analysis expressed that the people in the research area will have more chances of tangible and intangible loss due to disaster-induced health problems. The perceived health risk will be increased with the increased age of respondents that describe the old people had less health capacity to cope with this kind of adverse situation, decreased education level, marital status like from married to divorced or widow, decreased income level. Public Education, increased employment facilities, emergency service planning, and the implementation of training systems can be done to reduce health and other impacts by communicating information about disaster health risks to people. Health and emergency management authorities should improve disaster mitigation and adaptation policies at the local and wider scale to raise awareness of the health risks related to disasters and promote effective adaptive behaviors by adaptation policy. It can be accomplished by increasing the general understanding of present vulnerability, susceptibility, severity and urgency related to disaster related health risks. Efforts are needed to identify the current gaps so that information can be disseminated to respondents in a format that is accessible, easily understood and retained. To reduce the vulnerability of disaster livelihood resilience, mitigation strategies need to be developed through new policies, innovative and advanced technologies, and a new lifestyle. Community-based solutions should be developed and instigated to make sure that public can take in decisions affecting their well-being. Health issues should be included in any climate change related adaptation plan. The study indicates that study area people are not concerned about disaster, vulnerability and its impact on health. Public health and emergency management officials have an important role in communicating information about climate change health risks to research area. However, the findings also suggest that if health authorities in Amtoli are providing information to the public through their rural health models and innovation, programs, social media and websites, local newspapers addressing disaster health risk issues, it will be much better for quick adaptation. Furthermore, many people were not taking the needed actions to adapt to disaster related health risks. It is necessary that the existing efforts to change the behaviors of some vulnerable populations, such as seniors, have limited efficiency to lessen their vulnerability. Furthermore, widespread and vigorous research is needed to better understand the link between disaster and health. Unless steps are taken and put in place immediately to mitigate and adapt to disaster in coastal areas, the study area will have to recompense a heavy fee for economic loss and human lives.
To sum up the following recommendations are made regarding disaster and health vulnerability: 1. Improving the income level by public investment in empowering people and enhancing human capacity through education, 2. existing gender specific measures like: divorce rate, gender equality, different interventions, policies, and strategies need to work simultaneously to enhance women resiliency to disaster. It is significant to emphasise understanding and associate communities to effectively prepare for, respond to, and recover from the potential impacts of recurring disasters on health, well-being, and quality of life. Strategies could be included for assessing vulnerabilities and evolving adaptation strategies, capacity development of health professionals and suitable disaster risk reduction/ management programs. Without this targeted focus, communities will continue to experience the substantial losses and risks to health arising from disaster.
Climate change has been regarded as the largest global health challenge in the 21st century. Bangladesh is regarded as one of the nation’s most severely impacted by climate-related calamities worldwide. This country is physically vulnerable to climate change due to its unfavourable geographic location and low, flat topography (Rahman, 2017). In addition, several socioeconomic factors, including high population density, extreme poverty, gender inequality, reliance on climate-vulnerable livelihoods sectors, particularly agriculture, fishing, and hunting and gathering, institutional insufficiency, and a lack of adaptation funds, are making people more susceptible to harm (Khan et al., 2011). The effects of climate change are complex, dynamic, and more acutely felt in underdeveloped countries and communities, which might influence people's health and natural systems. Due to its fragile geographic location, Bangladesh, one of the South Asian nations, has begun to experience significant public health effects of climate change (Hasib and Chathoth, 2016). The Interco relations between the potential consequences of a changing climate; the communities are exposed to both positive and negative variables. These include sensitivity to increasing prevalence of health concerns and the capacity to plan for responding to these threats (Ebi and Bowen, 2016). The biggest danger to public health in the twenty-first century was climate change, linked to environmental degradation and harmful health effects (Patrice et al., 2021). Changes in freshwater resources, food availability, and access to proper healthcare facilities are the climate change effects on human health that are most concerning (Few, 2007). Vulnerability is considered from the viewpoint of the health sector as the totality of all risk and protective variables that determine whether a person or group suffers negative health effects from exposure to an extreme event (Balbus and Malina, 2009). Four factors must be geographically integrated to measure the health susceptibility to climate change: the actual climatic change, any future ecological change, the presence of human disease, and human habitation (Sari et al., 2003).
The IPCC's predicted changes to the global climate might indirectly or directly impact human health (Kim et al., 2014). Infectious disease outbreaks and transmission, thermal stress-related mortality from extremely high temperatures, and mortality and morbidity from extreme weather events like floods and storms are some of the direct effects on health. On the other hand, reproduction and spread of microorganisms and vectors, as well as harsh climatic conditions, indirectly impact health. The most vulnerable populations in affluent nations as well as low income countries, where the ability for adaptation is weakest, are anticipated to be affected by the majority of the negative consequences of climate change on health (Haines et al., 2006). Over 70% of respondents in the United States thought their jurisdiction (a county or city) would face severe adverse health consequences related to climate change during the following two decades (Akerlof et al., 2010). During severe occurrences, the risk of negative health outcomes was raised for the underprivileged, pregnant women, children, those with chronic illnesses, and people with mobility or cognitive impairments (Balbus and Malina, 2009). Additionally, vulnerability is influenced by the social determinants of health, including social capability, social norms, culture, healthcare services, access to and quality of education, transportation alternatives and most importantly, availability of resources (Braveman and Gottlieb, 2014). A disaster can have many different effects on a community, so the main goal of disaster management from a public health perspective is to evaluate the effectiveness of disaster relief programs, assess the needs of populations affected by disasters, match resources to those needs, prevent further negative health effects, implement disease control strategies for clearly defined problems, and improve contingency plans for various types of future disasters (Leppold et al., 2022). Although adaptation measures present practical challenges, especially in low-income nations, they should be prepared to mitigate some negative effects. In order to improve health, low-income countries should identify cost-effective intervention options in the health sector, such as early planning, which can help reduce future negative health impacts, and mitigation strategies, such as using a variety of renewable energy sources, which can both address climate change and improve health by reducing pollution. However, efficient emergency medical response depends on identifying these many medical and health issues before they occur and delivering the necessary interventions (relief supplies, equipment, and personnel) at the exact moments and locations where they are most required (Beyramijam et al., 2021).
The Bangladeshi government has acknowledged the difficulties in addressing the health-related risks brought on by the various disasters the nation has already experienced and has taken several good initiatives to reduce such risks. The 2008 Bangladesh Climate Change Strategy and Action Plan emphasized the necessity of putting in place surveillance systems for potential disease outbreaks, both old and new, and made sure health services are ready to handle future demands. Bangladesh is among the first nations to create a Climate Change and Health Promotion Unit (CCHPU) under the Ministry of Health and Family Welfare. The CCHPU's mandate is to conduct research, assess health promotion and climate change programs, and monitor such programs. To close a critical knowledge gap in the health field in the context of climate change, the Ministry of Environment has determined the necessity of conducting extensive statewide research focusing on climate-sensitive illnesses (Limin and Muthukumara, 2014). But even so, Disaster-related health concerns in Bangladesh are frequently underappreciated and understudied. Most of the time, when dealing with other disaster-related effects, this sector is given the least amount of attention. In addition, there is a significant lack of understanding among coastal residents regarding the health risks associated with disasters and risk control strategies. It is crucial to evaluate current vulnerabilities, understand how coastal residents are affected by health risks, and comprehend how the current healthcare system responds to their demands. The aim of this study to find out the existing and the perceived health vulnerability of the Amtoli union those are arisen due to disaster situations and after that, analyze the relationship between respondents’ socioeconomic characteristics with actual health vulnerability and perceived health vulnerability to highlight the triggering factor which may instigate the health vulnerability during disaster time towards the worst level.
II. Materials and Methods
Description of the study area
Barguna district lies on the bank of Payra river. This District has a total area of 1939.39 square kilometres. The area is located with latitude of 22.158°N and longitude of 90.1264°E. The study was conducted at Amtoli union of Amtoli upazila under Barguna district (Figure 01). Amtoli union has 50.59 square kilometres and literacy is 50.6%. Amtoli union was purposively selected for the study, as it is located near the Bay of Bengal and frequently affected by different kinds of disasters, especially water-related disasters. The average annual temperature was 26.0 °C and precipitation rate was 2516mm annually (BMD, 2015). Most common climatic events are floods, cyclones, heavy rainfall, river erosion, epidemic etc. CARE Bangladesh (2020) stated that Amtoli union was severely affected by the recent severe cyclone, known as super cyclone Amphan (2020). The lower part of Amtoli union was flooded, several houses, trees and schools collapsed due to strongest cyclonic storm. Some families were in waterlogged condition for several days due to high tide. It caused multiple health issues with massive damage and illness, potential disease outbreaks, disruption of healthcare facilities etc., along the study location. This was the reason for selecting the Amtoli Union for study area.
Figure 01. Study area map of Amtoli Union (see in pdf)
Data collecting instrument
A structured interview schedule was used as the data-gathering instrument. The interview schedule was carefully prepared, considering the objectives of the study. Qualitative methods such as Key informant interviews and focus group discussions were implied to get a clear picture of this situation.
Measurement of Variables
Measurement of independent variables
Sl. No Variable Measurement technique
1 Age Year
2 Education Illiterate=1, only sign=2, primary=3, secondary=4, higher secondary=5, graduate=6
3 Gender Male=1, Female=2
4 Marital status Single=1, Married=2, Divorced=3, Widow=4
5 Religion Muslim=1, Hindu=2, Christian=3, Buddhist=4
6. Income Taka
Measurement of Dependent variable
Actual Health Vulnerability Index: Actual health Vulnerability index was measured by computing an ‘obtain score’ and obtainable score based on total number of questionnaires. Here, used a formula to measure actual health vulnerability index.
AHVI= ∑ Score obtain per statement/Obtainable score
There were 26 items were taken, which were calculated in an excel sheet. Some codes were used to measure VI (Vulnerability Index). They were 0 and 1. Then calculated as the summation of score obtained per statement (26 items) divided by obtainable score (26 items). Then the result of vulnerability index was made (Table 01).
Table 01. Actual Health Vulnerability Index (see in pdf)
Perceived Health Vulnerability Index:
The household survey and KII questionnaires identified the perceived health vulnerability. The questionnaires collected future health problems, loss of life, physical capability, different contagious diseases, facilities of medical treatment, financial ability, Govt. health policies, etc. Here, used a formula to measure perceived health vulnerability index.
PHVI= ∑ Score obtain per statement/Obtainable score
There were 10 items taken and these items were calculated in excel sheet. Some codes were used to measure PRI (Perceived Risk Index). They were 0 and 1. Then calculated the summation of score obtained per statement (10 items) and this was divided by obtainable score (10 items). Then the result of perceived risk index was made (Table 02).
Table 02. Perceived Health Vulnerability (see in pdf)
Statistical tests
Firstly the data were inputted into a master sheet which was obtained from the respondents then compiled, tabulated and analyzed by the objectives of the study. Mean, percentage distribution and standard deviation those statistical methods were used in describing the dependent variables. Tables were used to presenting the data. Pearson‖s Product Moment Coefficients of correlation (r) analysis was used for exploring the relationship between the selected characteristics of the respondents with their actual health vulnerability and perceived health vulnerability due to climate change induced disaster.
III. Results and Discussion
Socio-economic profile of respondents
A total of 61 per cent of the survey respondents were male and 39 percent were female. Now in Bangladesh, the ratio of men and women is same, but still, the male dominated society doesn’t want to permit women for their freedom. Additionally, climate variability has pushed women into a vulnerable and marginalized position in Bangladesh. Especially natural disasters and man-made disasters like socially constructed systems had made the situation possible where climate change plays a key role in instigating the vulnerabilities of woman (Rahman, 2013).
In Table 03 It was found that 28.5 percent of respondents were category -2 aged, 37.5 percent were category -3 aged, and the rest 21.5 percent respondent’s age were upper 50 years. Most of the respondents in the study area age were between 36 to 50 or middle aged. Climate related injury and illness were mostly felt by children and elderly (UNICEF, 2021). Older people, especially those already in poor health, are particularly vulnerable to climate-related events and it will be severed over the next 20-30 years (Oven et al., 2012). Furthermore, the health of older adults can be disproportionately affected by environmental hazards associated with climate change and air pollution (e.g., increasing temperatures, extreme events and poor air quality (Benivolenj and Deregni, 2019)
Educational background of the farmers was classified into six categories: illiterate 51.5% can sign only 19%, primary level 3%, 10.5% of respondents have completed high school/secondary school, Higher secondary School percentage 11.5% and only 4.5% respondents appeared to university. Data showed that half of the respondent (51.5 percent) in this study area had no education. People with higher education levels were more likely to make efforts to health response, as they had higher perceived benefits and perceived severity when facing climate change (Li et al., 2021). Societies can improve the most effective long-term defense against the dangers by strengthening human capacity, primarily through education about risk perception, skills and knowledge, indirectly reducing poverty and improving health and resources (Muttarak and Lutz, 2014). Educational programs and policies are crucial public health interventions (Han and Truma, 2015).
In the study area, maximum respondents were married (80%). As the respondents were between 36 to 50 or middle-aged, it was natural that according to Bangladesh culture, middle-aged people must be married or widowed. On the other hand, most of the respondents in study area were Muslim (95.5%) (Table 03).
Table 03. Socioeconomic characteristics of respondent (see in pdf)
In analyses, stratified information given that the respondent’s monthly household income (BDT) was collected to know their socio-economic status. The people were categorized into three income groups and their response has been recorded: ‘low income’ (Taka 1200-7000 thousand), ‘medium income’ (Taka 7000-13000 thousands) and ‘high income’ (above Taka 13000 thousands). Highest portion of farmers (90.5 percent) were in low to medium income category. Due to low social and economic status, the respondents of study area could not properly fight against health problems raised from different kinds of adverse situations. People with low incomes could not invest large amounts of money in healthcare issues. The higher economic group was reported to have more resources to respond to climate change than the lower economic group (Li et al., 2021). Balasubramanian (2018) express that Climate change had not affected the world equally and people of lower socioeconomic status had experienced more negative effects than others. The overall balance of climate change is likely to affect the populations in low-income countries and particularly vulnerable to the adverse effects (Moreno, 2006; Hanies, 2006).
Actual health vulnerability index
Vulnerability is the fundamental underlying risk factor. It indicates the negative set of conditions of something or someone. From this analysis, it was shown that in this study area, people suffered from diseases due to disaster and following fewer shelter facilities, less medical facilities, no savings and unavailability of fresh water and hygienic toilet system were the most negative set of conditions that create them more vulnerable towards adverse situation (Table 04).
Table 04. Measurement of Actual Health Vulnerability of Study area’s People (see in pdf)
Perceived health vulnerability index
Perceived risk is a subjective judgment of an individual, an individual’s perceived susceptibility to a threat is a key component of many health behavior change theories (Ferrer and Klein, 2015). This study found out the study area’s people perception of their future disaster induced health vulnerability (Table 05). The result showed that people in study area thought there would be more chances of live loss due to disaster-induced health problems in the future. On the other hand, as per respondents’ perception, in future there will less financial vulnerability than present situation.
Table 05. Measurement of Perceived Health Vulnerability of Study area’s People (see in pdf)
Relationship between selected characteristics of the respondents and Actual Health Vulnerability
Pearson Product Moment co-efficient was computed to determine the extent of relationship between the dependent variable and independent variables (at 1% and 5% level of significance). The results of correlation analysis are shown in Table 06.
Table 06. Relation between the demographic characteristics of the respondents with their actual health vulnerability (see in pdf)
Respondent’s education and their actual health vulnerability: The co-efficient of correlation between education and their health vulnerabilities was negative and significant at the 1% probability level (r = -.268”). This indicates that level of vulnerabilities will increase with decreasing of educational level. Uneducated people did not know how to mitigate vulnerabilities to fight against disaster. So, if the percentage of educated people increases in the study area, they will be more functional before, during and after disaster.
Respondent’s marital status and their actual health vulnerabilities: The co-efficient correlation between marital status and their health vulnerabilities was positive and significant at the 1% probability level (r = .211”). This data indicates that vulnerabilities will increase if the respondent's marital status becomes single and married to divorced and widow. Widow and divorced women and men are more vulnerable because of absence of supporting person and the most considered factor is age issue.
Respondent’s income and their actual health vulnerabilities: The co-efficient correlation between income and their health vulnerabilities was negative and significant at the 1% probability level (r = -.312”). This indicated that vulnerabilities increase with decreasing in income level. Because if the affected area’s people had low-income level, then they have less capacity to fight against disaster and it will increase their vulnerabilities in an ongoing process.
Respondent’s gender and their actual health vulnerabilities: The co-efficient of correlation between gender and their health vulnerabilities was negative and significant at the 5% level of probability (r = -.158’). This negative relationship indicates that vulnerabilities will increase if the study area’s male population decreases or the female population increases. As women have less strength and most of them have no proper knowledge to reduce health risks arising from different kinds of adverse situations, they are more vulnerable than males.
Relationship between selected characteristics of the respondents and Perceived Health Vulnerability
Pearson Product Moment co-efficient was computed to find out the extent of relationship between the dependent variable and independent variables (at 1% and 5% level of significance). The results of correlation analysis are shown in Table 07.
Table 07. Relation between the demographic characteristics of the respondents and perceived health Vulnerability (see in pdf)
Respondent’s age and their perceived health Vulnerability: The co-efficient correlation between age and their perceived health vulnerability was negative and significant at the 1% probability level of (r = -.320”). It indicates that the perceived health risk will be increased if the age of respondents will decrease. Because most of the respondents in the study were middle-aged, they thought that old people had less health capacity to cope with this kind of adverse situation.
Respondent’s education and their perceived health Vulnerability: The co-efficient correlation between education and their perceived health vulnerability was positive and significant at the 1% probability level (r = .346”). It means that assessing the possibility of suffering negative health events will be increased with the increase of educational level of respondents. Because if the people in study area will literate, they will understand how to deal with an emergency and disaster arising from it.
Respondent’s marital status and their perceived health Vulnerability: The co-efficient of correlation between marital status and perceived health vulnerability was positive and significant at the 1% level of probability (r = .357”). According to the result, the perceived health risk of respondents will be increased with the change of respondent’s marital status, like from married to divorced or widow. Because most of the divorced and widow in the study area was woman and the vulnerability of woman is always greater than the man.
Respondent’s income and their perceived health Vulnerability: The co-efficient of correlation between income and their perceived health vulnerability was negative and significant at the 1% level of probability (r = -.461”). Decreasing the level of income as well as increasing the perceived health risks. Because if respondents cannot take proper treatment for their existing disabilities, then it will increase their vulnerability towards disaster or any kind of negative situation in the near future.
IV. Conclusion
The analysis expressed that the people in the research area will have more chances of tangible and intangible loss due to disaster-induced health problems. The perceived health risk will be increased with the increased age of respondents that describe the old people had less health capacity to cope with this kind of adverse situation, decreased education level, marital status like from married to divorced or widow, decreased income level. Public Education, increased employment facilities, emergency service planning, and the implementation of training systems can be done to reduce health and other impacts by communicating information about disaster health risks to people. Health and emergency management authorities should improve disaster mitigation and adaptation policies at the local and wider scale to raise awareness of the health risks related to disasters and promote effective adaptive behaviors by adaptation policy. It can be accomplished by increasing the general understanding of present vulnerability, susceptibility, severity and urgency related to disaster related health risks. Efforts are needed to identify the current gaps so that information can be disseminated to respondents in a format that is accessible, easily understood and retained. To reduce the vulnerability of disaster livelihood resilience, mitigation strategies need to be developed through new policies, innovative and advanced technologies, and a new lifestyle. Community-based solutions should be developed and instigated to make sure that public can take in decisions affecting their well-being. Health issues should be included in any climate change related adaptation plan. The study indicates that study area people are not concerned about disaster, vulnerability and its impact on health. Public health and emergency management officials have an important role in communicating information about climate change health risks to research area. However, the findings also suggest that if health authorities in Amtoli are providing information to the public through their rural health models and innovation, programs, social media and websites, local newspapers addressing disaster health risk issues, it will be much better for quick adaptation. Furthermore, many people were not taking the needed actions to adapt to disaster related health risks. It is necessary that the existing efforts to change the behaviors of some vulnerable populations, such as seniors, have limited efficiency to lessen their vulnerability. Furthermore, widespread and vigorous research is needed to better understand the link between disaster and health. Unless steps are taken and put in place immediately to mitigate and adapt to disaster in coastal areas, the study area will have to recompense a heavy fee for economic loss and human lives.
To sum up the following recommendations are made regarding disaster and health vulnerability: 1. Improving the income level by public investment in empowering people and enhancing human capacity through education, 2. existing gender specific measures like: divorce rate, gender equality, different interventions, policies, and strategies need to work simultaneously to enhance women resiliency to disaster. It is significant to emphasise understanding and associate communities to effectively prepare for, respond to, and recover from the potential impacts of recurring disasters on health, well-being, and quality of life. Strategies could be included for assessing vulnerabilities and evolving adaptation strategies, capacity development of health professionals and suitable disaster risk reduction/ management programs. Without this targeted focus, communities will continue to experience the substantial losses and risks to health arising from disaster.
Article Citations
MLA
Debnath, P. et al. “Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh”. Journal of Science, Technology and Environment Informatics 12(02) (2023): 806-820.
APA
Debnath, P., Siddiqua, A. and Biswas, A. (2023). Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh. Journal of Science, Technology and Environment Informatics, 12(02), 806-820.
Chicago
Debnath, P., Siddiqua, A. and Biswas, A. “Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh.” Journal of Science, Technology and Environment Informatics 12(02) (2023): 806-820.
Harvard
Debnath, P., Siddiqua, A. and Biswas, A. 2023. Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh. Journal of Science, Technology and Environment Informatics, 12(02), pp. 806-820.
Vancouver
Debnath P, Siddiqua A and Biswas A. Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh. Journal of Science, Technology and Environment Informatics. 2023 August, 12(02): 806-820.
Debnath, P. et al. “Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh”. Journal of Science, Technology and Environment Informatics 12(02) (2023): 806-820.
APA
Debnath, P., Siddiqua, A. and Biswas, A. (2023). Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh. Journal of Science, Technology and Environment Informatics, 12(02), 806-820.
Chicago
Debnath, P., Siddiqua, A. and Biswas, A. “Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh.” Journal of Science, Technology and Environment Informatics 12(02) (2023): 806-820.
Harvard
Debnath, P., Siddiqua, A. and Biswas, A. 2023. Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh. Journal of Science, Technology and Environment Informatics, 12(02), pp. 806-820.
Vancouver
Debnath P, Siddiqua A and Biswas A. Actual health vulnerability vs perceived health vulnerability due to disaster at Amtali union, Bangladesh. Journal of Science, Technology and Environment Informatics. 2023 August, 12(02): 806-820.
References
1]. Ahsan, M. N. and Warner, J. (2014). The socioeconomic vulnerability index: a pragmatic approach for assessing climate change led risks–A case study in the south-western coastal Bangladesh. International Journal of Disaster Risk Reduction. 8, 32–49. https://doi.org/10.1016/j.ijdrr.2013.12.009
[2]. Akerlof, K., DeBono, R., Berry, P., Leiserowitz, A., Roser-Renouf, C., Clarke, K. L., Rogaeva, A., Nisbet, M. C., Weathers, M. R. and Maibach, E. W. (2010). Public Perceptions of Climate Change as a Human Health Risk: Surveys of the United States, Canada and Malta. International Journal of Environmental Research and Public Health. 7(6), 2559-2606.
https://doi.org/10.3390/ijerph7062559.
[3]. Alam, E. and Collins, A. E. (2010). Cyclone disaster vulnerability and response experiences in coastal Bangladesh. Disasters, 34, 931–954. https://doi.org/10.1111/j.1467-7717.2010.01176.x
[4]. Armas, I. and Avram, E. (2009). Perception of flood risk in Danube Delta, Romania, Nat. Hazards 50(2), 269–287. https://doi.org/10.1007/s11069-008-9337-0
[5]. Balasubramanian, M. (2018). Climate change, famine, and low-income communities challenge Sustainable Development Goals. In The Lancet Planetary Health, 2(10), e421–e422. https://doi.org/10.1016/S2542-5196(18)30212-2
[6]. Balbus, J. M. and Malina, C. (2009). Identifying vulnerable subpopulations for climate change health effects in the United States. Journal of occupational and environmental medicine, 51(1), 33–37. https://doi.org/10.1097/JOM.0b013e318193e12e
[7]. Balica, S. F., Wright, N. G. and van der Meulen, F. (2012). A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards, 64, 73–105. https://doi.org/10.1007/s11069-012-0234-1
[8]. Benivolenja, M. A. and Derigne, L. A. (2019). The impact of climate change and natural disasters on vulnerable populations: A systematic review of literature. Journal of Human Behavior in the Social Environment, 29(2), 266–281. https://doi.org/10.1080/10911359.2018.1527739
[9]. Beyramijam, M., Farrokhi, M., Ebadi, A., Masoumi, G. and Khankeh, H. R. (2021). Disaster preparedness in emergency medical service agencies: A systematic review. Journal of education and health promotion, 10, 258.
[10]. Birkmann, J. and Coauthors. (2013). Framing vulnerability, risk and societal responses: the MOVE framework. Natural Hazards, 67, 193–211. https://doi.org/10.1007/s11069-013-0558-5
[11]. Bishawjit, M., Bayes, A. and Joachim, V. (2017). Living with the risks of cyclone disasters in the south-western coastal region of Bangladesh. Environments, 4(1), 13. https://doi.org/10.3390/environments4010013
[12]. BMD. (2015). Bangladesh Meteorological Department. http://www.bmd.gov.bd
[13]. Braveman, P. and Gottlieb, L. (2014). The social determinants of health: it's time to consider the causes of the causes. Public health reports (Washington, D.C.: 1974), 129 Suppl 2 (Suppl 2), 19–31. https://doi.org/10.1177/00333549141291S206
[14]. CARE Bangladesh. (2020). http://www.carebangladesh.org
[15]. Ebi, K. L. and Bowen, K. (2016). Extreme events as sources of health vulnerability: Drought as an example. Weather and Climate Extremes, 11, 95–102. https://doi.org/10.1016/j.wace.2015.10.001
[16]. Ferrer, R. and Klein, W. M. (2015). Risk perceptions and health behavior. Current opinion in psychology, 5, 85–89. https://doi.org/10.1016/j.copsyc.2015.03.012
[17]. Few, R. (2007). Health and climatic hazards: Framing social research on vulnerability, response and adaptation. Global Environmental Change, 17(2), 281–295. https://doi.org/10.1016/j.gloenvcha.2006.11.001
[18]. Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L. and Lewis, B. (2011) A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management, 8, 1–22. https://doi.org/10.2202/1547-7355.1792
[19]. Hahn, Riederer, A. M. and Foster, S. O. (2009). The livelihood vulnerability index: a pragmatic approach to assessing risks from climate variability and change—a case study in Mozambique, Global Environmental Change, 19, 74–88. https://doi.org/10.1177/0020731415585986
[20]. Haines, A., Kovats, R. S., Campbell-Lendrum, D. and Corvalan, C. (2006). Climate change and human health: Impacts, vulnerability and public health. Public Health, 120(7), 585–596. https://doi.org/10.1016/j.puhe.2006.01.002
[21]. Han, R. A. and Truman, B. I. (2015). Education improves public health and promotes health equity. International Journal of Health Services, 45(4), 657–678. https://doi.org/10.1177/0020731415585986
[22]. Hasib, E. and Chathoth, P. (2016). Health Impact of Climate Change in Bangladesh: A Summary. Current Urban Studies, 04(01), 1–8. https://doi.org/10.4236/cus.2016.41001
[23]. Helldén, D., Andersson, C., Nilsson, M., Ebi, K. L., Friberg, P. and Alfvén, T. (2021). Climate change and child health: a scoping review and an expanded conceptual framework. The Lancet Planetary Health, 5(3), e164–e175. https://doi.org/10.1016/S2542-5196(20)30274-6
[24]. Ho, M. C., Shaw, D., Lin, S. and Chiu, Y. C. (2008). How do disaster characteristics influence risk perception? Risk Analysis, 28, 635–643. https://doi.org/10.1111/j.1539-6924.2008.01040.x
[25]. Hosseini, K. A., Hosseini, M., Izadkhah, Y. O., Mansouri, B. and Shaw, T. (2014). Main challenges on community-based approaches in earthquake risk reduction: case study of Tehran, Iran. International Journal of Disaster Risk Reduction, 8, 114–124. https://doi.org/10.1016/j.outlook.2020.08.001
[26]. Kazmierczak, A. and Cavan, G. (2011). Surface water flooding risk to urban communities: analysis of vulnerability, hazard and exposure. Landscape and Urban Planning, 103, 185–197. https://doi.org/10.1016/j.landurbplan.2011.07.008
[27]. Khan, A. E., Ireson, A., Kovats, S., Mojumder, S. K., Khusru, A., Rahman, A. and Vineis, P. (2011). Drinking water salinity and maternal health in coastal Bangladesh: Implications of climate change. Environmental Health Perspectives, 119(9), 1328–1332. https://doi.org/10.1289/ehp.1002804
[28]. Kim, K., Kabir. E. and Jahan, S.A. (2014). A Review of the Consequences of Global Climate Change on Human Health. Journal of Environmental Science and Health, 32(3), 299-318, https://doi.org/10.1080/10590501.2014.941279
[29]. Kovats, S., Ebi, K. L. and Menne, B. (2003). Methods of assessing human health vulnerability and public health adaptation to climate change. World Health Organization. Regional Office for Europe. https://apps.who.int/iris/handle/10665/107538
[30]. Leppold, C., Gibbs, L., Block, K., Reifels, L., and Quinn, P. (2022). Public health implications of multiple disaster exposures. The Lancet. Public health, 7(3), e274–e286. https://doi.org/10.1016/S2468-2667(21)00255-3
[31]. Li, W., Yuan, K., Yue, M., Zhang, L. and Huang, F. (2021). Climate change risk perceptions, facilitating conditions and health risk management intentions: Evidence from farmers in rural China. Climate Risk Management, 32, 100283. https://doi.org/10.1016/j.crm.2021.100283
[32]. Limin, W. and Muthukumara, M. (2014). Climate change and health impacts: how vulnerable is Bangladesh and what needs to be done. Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/836581468004786339/Climate-change-and-health-impacts-how-vulnerable-is-Bangladesh-and-what-needs-to-be-done
[33]. Mahmud, T. and Prowse, M. (2012). Corruption in cyclone preparedness and relief efforts in coastal Bangladesh: Lessons for climate adaptation?. Global Environmental Change. 22, 933–943. https://doi.org/10.1016/j.gloenvcha.2012.07.003
[34]. Miceli, R., Sotgiu, I. and Settanni, M. (2008). Disaster preparedness and perception of flood risk: a study in an alpine valley in Italy, Journal of Environmental Psychology, 28(2), 164–173. https://doi.org/10.1016/j.jenvp.2007.10.006
[35]. Moreno, A. R. (2006). Climate change and human health in Latin America: Drivers, effects, and policies. Regional Environmental Change, 6(3), 157–164. https://doi.org/10.1007/s10113-006-0015-z
[36]. Muttarak, R. and Lutz, W. (2014). Is education a key to reducing vulnerability to natural disasters and hence unavoidable climate change? Ecology and Society, 19(1). https://doi.org/10.5751/ES-06476-190142
[37]. Mwale, F. D., Adeloye, A. J. and Beevers, L. (2015). Quantifying vulnerability of rural communities to flooding in SSA: a contemporary disaster management perspective applied to the Lower Shire Valley, Malawi. International Journal of Disaster Risk Reduction, 12, 172–187. https://doi.org/10.1016/j.ijdrr.2015.01.003
[38]. Nhuan, M. T., Tue, N. T., Hue, N. T. H., Quy, T. D. and Lieu, T.M. (2016). An indicator-based approach to quantifying the adaptive capacity of urban households: the case of Da Nang city, Central Vietnam. Urban Climate, 15, 60–69. https://doi.org/10.1016/j.uclim.2016.01.002
[39]. Oven, K. J., Curtis, S. E., Reaney, S., Riva, M., Stewart, M. G., Ohlemüller, R., Dunn, C. E., Nodwell, S., Dominelli, L. and Holden, R. (2012). Climate change and health and social care: Defining future hazard, vulnerability and risk for infrastructure systems supporting older people’s health care in England. Applied Geography, 33(1), 16–24. https://doi.org/10.1016/j.apgeog.2011.05.012
[40]. Patrice, K. N., Breakey, S., Tagliareni, M. E., Simmonds, K. and Sabo, K. K. (2021). Climate Change and Population Health: Incorporating Stages of Nursing's Political Development. Nursing Outlook, 69(1), 65-73. https://doi.org/10.1016/j.outlook.2020.08.001
[41]. Rahman, M. S. (2013). Climate Change, Disaster and Gender Vulnerability: A Study on Two Divisions of Bangladesh. American Journal of Human Ecology, 2(2), 72–82. https://doi.org/10.11634/216796221302315
[42]. Rahman, S. (2017). Children, Youth & DRR in Bangladesh, UNDRR.
[43]. Sari, K., Kristie L. E. and Bettina, M. (2003). Methods of assessing human health vulnerability and public health adaptation to climate change. World Health Organization. Regional Office for Europe.
[44]. Sattar, M. A. and Cheung, K. K. W. (2019). Tropical cyclone risk perception and risk reduction analysis for coastal Bangladesh: Household and expert perspectives, International Journal of Disaster Risk Reduction, 41, 101283. https://doi.org/10.1016/j.ijdrr.2019.101283
[45]. Terpstra, T. and Gutteling, J. M. (2008). Households’ perceived responsibilities in flood risk management in The Netherlands. International Journal of Water Resources Development, 24 (4), 555–565. https://doi.org/10.1080/07900620801923385
[46]. UNICEF (2021). The Climate Crisis is a Child Rights Crisis: Introducing the Children’s Climate Risk Index. New York: United Nations Children’s Fund (UNICEF)
[47]. Wisner, B., Blaikie, P., Cannon, T. and Davis, I. (2004). At Risk: Natural Hazards, People’s Vulnerability and Disasters, second ed., Routledge, London.
[48]. Yu, H., Wang, B., Zhang, Y. J., Wang, S. and Wei, Y. M. (2013). Public perception of climate change in China: results from the questionnaire survey. Natural Hazards, 69, 459–472. https://doi.org/10.1007/s11069-013-0683-1, https://doi.org/10.1007/s11069-013-0711-1
[49]. Zhou, Y., Liu, Y., Wu, W. and Li, N. (2015). Integrated risk assessment of multi-hazards in China, Natural Hazards, 78, 257–280. https://doi.org/10.1007/s11069-015-1713-y
[2]. Akerlof, K., DeBono, R., Berry, P., Leiserowitz, A., Roser-Renouf, C., Clarke, K. L., Rogaeva, A., Nisbet, M. C., Weathers, M. R. and Maibach, E. W. (2010). Public Perceptions of Climate Change as a Human Health Risk: Surveys of the United States, Canada and Malta. International Journal of Environmental Research and Public Health. 7(6), 2559-2606.
https://doi.org/10.3390/ijerph7062559.
[3]. Alam, E. and Collins, A. E. (2010). Cyclone disaster vulnerability and response experiences in coastal Bangladesh. Disasters, 34, 931–954. https://doi.org/10.1111/j.1467-7717.2010.01176.x
[4]. Armas, I. and Avram, E. (2009). Perception of flood risk in Danube Delta, Romania, Nat. Hazards 50(2), 269–287. https://doi.org/10.1007/s11069-008-9337-0
[5]. Balasubramanian, M. (2018). Climate change, famine, and low-income communities challenge Sustainable Development Goals. In The Lancet Planetary Health, 2(10), e421–e422. https://doi.org/10.1016/S2542-5196(18)30212-2
[6]. Balbus, J. M. and Malina, C. (2009). Identifying vulnerable subpopulations for climate change health effects in the United States. Journal of occupational and environmental medicine, 51(1), 33–37. https://doi.org/10.1097/JOM.0b013e318193e12e
[7]. Balica, S. F., Wright, N. G. and van der Meulen, F. (2012). A flood vulnerability index for coastal cities and its use in assessing climate change impacts. Natural Hazards, 64, 73–105. https://doi.org/10.1007/s11069-012-0234-1
[8]. Benivolenja, M. A. and Derigne, L. A. (2019). The impact of climate change and natural disasters on vulnerable populations: A systematic review of literature. Journal of Human Behavior in the Social Environment, 29(2), 266–281. https://doi.org/10.1080/10911359.2018.1527739
[9]. Beyramijam, M., Farrokhi, M., Ebadi, A., Masoumi, G. and Khankeh, H. R. (2021). Disaster preparedness in emergency medical service agencies: A systematic review. Journal of education and health promotion, 10, 258.
[10]. Birkmann, J. and Coauthors. (2013). Framing vulnerability, risk and societal responses: the MOVE framework. Natural Hazards, 67, 193–211. https://doi.org/10.1007/s11069-013-0558-5
[11]. Bishawjit, M., Bayes, A. and Joachim, V. (2017). Living with the risks of cyclone disasters in the south-western coastal region of Bangladesh. Environments, 4(1), 13. https://doi.org/10.3390/environments4010013
[12]. BMD. (2015). Bangladesh Meteorological Department. http://www.bmd.gov.bd
[13]. Braveman, P. and Gottlieb, L. (2014). The social determinants of health: it's time to consider the causes of the causes. Public health reports (Washington, D.C.: 1974), 129 Suppl 2 (Suppl 2), 19–31. https://doi.org/10.1177/00333549141291S206
[14]. CARE Bangladesh. (2020). http://www.carebangladesh.org
[15]. Ebi, K. L. and Bowen, K. (2016). Extreme events as sources of health vulnerability: Drought as an example. Weather and Climate Extremes, 11, 95–102. https://doi.org/10.1016/j.wace.2015.10.001
[16]. Ferrer, R. and Klein, W. M. (2015). Risk perceptions and health behavior. Current opinion in psychology, 5, 85–89. https://doi.org/10.1016/j.copsyc.2015.03.012
[17]. Few, R. (2007). Health and climatic hazards: Framing social research on vulnerability, response and adaptation. Global Environmental Change, 17(2), 281–295. https://doi.org/10.1016/j.gloenvcha.2006.11.001
[18]. Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L. and Lewis, B. (2011) A social vulnerability index for disaster management. Journal of Homeland Security and Emergency Management, 8, 1–22. https://doi.org/10.2202/1547-7355.1792
[19]. Hahn, Riederer, A. M. and Foster, S. O. (2009). The livelihood vulnerability index: a pragmatic approach to assessing risks from climate variability and change—a case study in Mozambique, Global Environmental Change, 19, 74–88. https://doi.org/10.1177/0020731415585986
[20]. Haines, A., Kovats, R. S., Campbell-Lendrum, D. and Corvalan, C. (2006). Climate change and human health: Impacts, vulnerability and public health. Public Health, 120(7), 585–596. https://doi.org/10.1016/j.puhe.2006.01.002
[21]. Han, R. A. and Truman, B. I. (2015). Education improves public health and promotes health equity. International Journal of Health Services, 45(4), 657–678. https://doi.org/10.1177/0020731415585986
[22]. Hasib, E. and Chathoth, P. (2016). Health Impact of Climate Change in Bangladesh: A Summary. Current Urban Studies, 04(01), 1–8. https://doi.org/10.4236/cus.2016.41001
[23]. Helldén, D., Andersson, C., Nilsson, M., Ebi, K. L., Friberg, P. and Alfvén, T. (2021). Climate change and child health: a scoping review and an expanded conceptual framework. The Lancet Planetary Health, 5(3), e164–e175. https://doi.org/10.1016/S2542-5196(20)30274-6
[24]. Ho, M. C., Shaw, D., Lin, S. and Chiu, Y. C. (2008). How do disaster characteristics influence risk perception? Risk Analysis, 28, 635–643. https://doi.org/10.1111/j.1539-6924.2008.01040.x
[25]. Hosseini, K. A., Hosseini, M., Izadkhah, Y. O., Mansouri, B. and Shaw, T. (2014). Main challenges on community-based approaches in earthquake risk reduction: case study of Tehran, Iran. International Journal of Disaster Risk Reduction, 8, 114–124. https://doi.org/10.1016/j.outlook.2020.08.001
[26]. Kazmierczak, A. and Cavan, G. (2011). Surface water flooding risk to urban communities: analysis of vulnerability, hazard and exposure. Landscape and Urban Planning, 103, 185–197. https://doi.org/10.1016/j.landurbplan.2011.07.008
[27]. Khan, A. E., Ireson, A., Kovats, S., Mojumder, S. K., Khusru, A., Rahman, A. and Vineis, P. (2011). Drinking water salinity and maternal health in coastal Bangladesh: Implications of climate change. Environmental Health Perspectives, 119(9), 1328–1332. https://doi.org/10.1289/ehp.1002804
[28]. Kim, K., Kabir. E. and Jahan, S.A. (2014). A Review of the Consequences of Global Climate Change on Human Health. Journal of Environmental Science and Health, 32(3), 299-318, https://doi.org/10.1080/10590501.2014.941279
[29]. Kovats, S., Ebi, K. L. and Menne, B. (2003). Methods of assessing human health vulnerability and public health adaptation to climate change. World Health Organization. Regional Office for Europe. https://apps.who.int/iris/handle/10665/107538
[30]. Leppold, C., Gibbs, L., Block, K., Reifels, L., and Quinn, P. (2022). Public health implications of multiple disaster exposures. The Lancet. Public health, 7(3), e274–e286. https://doi.org/10.1016/S2468-2667(21)00255-3
[31]. Li, W., Yuan, K., Yue, M., Zhang, L. and Huang, F. (2021). Climate change risk perceptions, facilitating conditions and health risk management intentions: Evidence from farmers in rural China. Climate Risk Management, 32, 100283. https://doi.org/10.1016/j.crm.2021.100283
[32]. Limin, W. and Muthukumara, M. (2014). Climate change and health impacts: how vulnerable is Bangladesh and what needs to be done. Washington, D.C.: World Bank Group. http://documents.worldbank.org/curated/en/836581468004786339/Climate-change-and-health-impacts-how-vulnerable-is-Bangladesh-and-what-needs-to-be-done
[33]. Mahmud, T. and Prowse, M. (2012). Corruption in cyclone preparedness and relief efforts in coastal Bangladesh: Lessons for climate adaptation?. Global Environmental Change. 22, 933–943. https://doi.org/10.1016/j.gloenvcha.2012.07.003
[34]. Miceli, R., Sotgiu, I. and Settanni, M. (2008). Disaster preparedness and perception of flood risk: a study in an alpine valley in Italy, Journal of Environmental Psychology, 28(2), 164–173. https://doi.org/10.1016/j.jenvp.2007.10.006
[35]. Moreno, A. R. (2006). Climate change and human health in Latin America: Drivers, effects, and policies. Regional Environmental Change, 6(3), 157–164. https://doi.org/10.1007/s10113-006-0015-z
[36]. Muttarak, R. and Lutz, W. (2014). Is education a key to reducing vulnerability to natural disasters and hence unavoidable climate change? Ecology and Society, 19(1). https://doi.org/10.5751/ES-06476-190142
[37]. Mwale, F. D., Adeloye, A. J. and Beevers, L. (2015). Quantifying vulnerability of rural communities to flooding in SSA: a contemporary disaster management perspective applied to the Lower Shire Valley, Malawi. International Journal of Disaster Risk Reduction, 12, 172–187. https://doi.org/10.1016/j.ijdrr.2015.01.003
[38]. Nhuan, M. T., Tue, N. T., Hue, N. T. H., Quy, T. D. and Lieu, T.M. (2016). An indicator-based approach to quantifying the adaptive capacity of urban households: the case of Da Nang city, Central Vietnam. Urban Climate, 15, 60–69. https://doi.org/10.1016/j.uclim.2016.01.002
[39]. Oven, K. J., Curtis, S. E., Reaney, S., Riva, M., Stewart, M. G., Ohlemüller, R., Dunn, C. E., Nodwell, S., Dominelli, L. and Holden, R. (2012). Climate change and health and social care: Defining future hazard, vulnerability and risk for infrastructure systems supporting older people’s health care in England. Applied Geography, 33(1), 16–24. https://doi.org/10.1016/j.apgeog.2011.05.012
[40]. Patrice, K. N., Breakey, S., Tagliareni, M. E., Simmonds, K. and Sabo, K. K. (2021). Climate Change and Population Health: Incorporating Stages of Nursing's Political Development. Nursing Outlook, 69(1), 65-73. https://doi.org/10.1016/j.outlook.2020.08.001
[41]. Rahman, M. S. (2013). Climate Change, Disaster and Gender Vulnerability: A Study on Two Divisions of Bangladesh. American Journal of Human Ecology, 2(2), 72–82. https://doi.org/10.11634/216796221302315
[42]. Rahman, S. (2017). Children, Youth & DRR in Bangladesh, UNDRR.
[43]. Sari, K., Kristie L. E. and Bettina, M. (2003). Methods of assessing human health vulnerability and public health adaptation to climate change. World Health Organization. Regional Office for Europe.
[44]. Sattar, M. A. and Cheung, K. K. W. (2019). Tropical cyclone risk perception and risk reduction analysis for coastal Bangladesh: Household and expert perspectives, International Journal of Disaster Risk Reduction, 41, 101283. https://doi.org/10.1016/j.ijdrr.2019.101283
[45]. Terpstra, T. and Gutteling, J. M. (2008). Households’ perceived responsibilities in flood risk management in The Netherlands. International Journal of Water Resources Development, 24 (4), 555–565. https://doi.org/10.1080/07900620801923385
[46]. UNICEF (2021). The Climate Crisis is a Child Rights Crisis: Introducing the Children’s Climate Risk Index. New York: United Nations Children’s Fund (UNICEF)
[47]. Wisner, B., Blaikie, P., Cannon, T. and Davis, I. (2004). At Risk: Natural Hazards, People’s Vulnerability and Disasters, second ed., Routledge, London.
[48]. Yu, H., Wang, B., Zhang, Y. J., Wang, S. and Wei, Y. M. (2013). Public perception of climate change in China: results from the questionnaire survey. Natural Hazards, 69, 459–472. https://doi.org/10.1007/s11069-013-0683-1, https://doi.org/10.1007/s11069-013-0711-1
[49]. Zhou, Y., Liu, Y., Wu, W. and Li, N. (2015). Integrated risk assessment of multi-hazards in China, Natural Hazards, 78, 257–280. https://doi.org/10.1007/s11069-015-1713-y
© 2023 The Authors. This article is freely available for anyone to read, share, download, print, permitted for unrestricted use and build upon, provided that the original author(s) and publisher are given due credit. All Published articles are distributed under the Creative Commons Attribution 4.0 International License.
Journal of Science, Technology and Environment Informatics, EISSN 2409-7632.