Natural and human-made disaster and associated health outcomes among community-dwelling older adults in India: Findings from LASI, 2017–18

T. Muhammad; Manacy Pai; Chanda Maurya; Shobhit Srivastava; Manish Kumar

doi:10.1371/journal.pone.0307371

Abstract

Background

Droughts, flash floods, rail accidents, and riots are relatively regular occurrences for those living in many low- and middle-income countries like India. While such natural and human-made disasters put everyone in harm’s way, their toll on specific segments of society–like older adults–is the heaviest. Therefore, in this study, we examine (1) the prevalence of natural and human-made disasters in India and (2) the association between natural and human-made disasters and several physical and mental health outcomes among older Indians.

Methods

A cross-sectional study was conducted utilizing data come from the 2017–18 wave 1 of the nationally representative Longitudinal Ageing Study in India, comprising a sample of 29,333 older adults (14,120 males and 15,213 females) aged 60 years and above. Multivariate random intercept multilevel logistic regression analysis is used to examine the association between natural and human-made disasters and poor self-rated health, difficulty in activities of daily living, difficulty in instrumental activities of daily living, communicable diseases, non-communicable diseases, depressive symptoms, and psychiatric disorder.

Results

Overall, 3.58% of older adults reported that they have encountered any type of natural or human-made disaster in the past five years. Compared to those who did not experience any (natural or human-made) disaster, older adults who experienced any disaster had a higher prevalence of poor self-rated health (33.4% vs 23.31%), difficulty in activities of daily living (33.94% vs 23.00%), difficulty in instrumental activities of daily living (60.09% vs 47.70%), communicable diseases (49.57% vs 25.86%), depressive symptoms (17.30% vs 8.06%) and psychiatric disorders (3.42% vs 2.78%). After adjusting for the selected variables and the contextual effect, the odds of poor self-rated health (1.64 [1.40, 1.92]), difficulty in activities of daily living and instrumental activities of daily living (1.89 [1.61, 2.21] and 1.63 [1.40, 1.89]), communicable and non-communicable diseases (2.12 [1.83, 2.46] and 1.38 [1.20, 1.60]), depressive symptoms and psychiatric disorder (1.67 [1.55, 2.05] and 1.52 [1.33, 2.18]) were significantly higher among older adults who experienced a natural or human-made disaster than their counterparts without such an experience.

Conclusions

Relative to their non-exposed counterparts, older Indians who survived natural or human-made disasters endured an inflated risk of poor self-rated health, functional difficulties, communicable and non-communicable diseases, depressive symptoms, and psychiatric disorders. As such, post-disaster efforts should be grounded in policies and programs that address disaster-related trauma and diseases and improve the functional, physical, and psychological facets of health among older disaster survivors.

Citation: Muhammad T, Pai M, Maurya C, Srivastava S, Kumar M (2024) Natural and human-made disaster and associated health outcomes among community-dwelling older adults in India: Findings from LASI, 2017–18. PLoS ONE 19(7): e0307371. https://doi.org/10.1371/journal.pone.0307371

Editor: De-Chih Lee, Dayeh University, TAIWAN

Received: February 17, 2022; Accepted: July 3, 2024; Published: July 18, 2024

Copyright: © 2024 Muhammad et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The study utilizes a secondary source of data that is freely available in public domain through request from https://iipsindia.ac.in/sites/default/files/LASI_DataRequestForm_0.pdf

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Background

The World Health Organization defines a disaster as any event that disrupts the daily functioning of a community or society, causing material, economic, or environmental losses, and overwhelming local capacity [1]. Disasters can be natural (e.g., earthquakes, tornadoes, and hurricanes) and human-made (e.g., armed conflicts, nuclear accidents, industrial explosions, infrastructural failures, environmental pollution) [1]. According to a National Portal of India report, 27 of 36 state and union territories remain prone to natural disasters [2]. Almost 60% of the Indian subcontinent remains vulnerable to flooding and earthquakes, 68% of the usable land is at risk of drought [3], and about 8% faces the risk of cyclones [4, 5]. In addition to natural calamities and the recent virus pandemic of COVID-19, Indians have lost lives and livelihoods to rail accidents, bridge collapses, public unrest, riots, and terrorism [4]. While such natural and human-made disasters put everyone in harm’s way, their toll on specific segments of society, like older adults, is the heaviest [6, 7].

Conceptual framework

We use the fundamental cause theory (FCT) [8] to argue that older adults are particularly susceptible to disasters and their resulting consequences. FCT theorists Link and Phelan [8] argue that social factors, such as socioeconomic status (SES), are associated with a multitude of resources that can be used to prevent risks, implement preventive measures, and maintain and enhance health, making them fundamental and persistent causes of disease. Higher-SES persons have access to “flexible” or “tranferrable” resources, like knowledge, power, high social stature, and ties to mainstream society, that they can use to their advantage in times of need to protect health [8]. Here, we argue that, like SES, age is a social factor that shapes access to essential resources needed to protect oneself from disasters. Like low SES, old age, which is a stigmatized social status [9], increases exposure to multiple risk factors (e.g., chronic disease, physical frailty, cognitive decline), and increasing age often is attached to loss of vital resources, like power, prestige, and mainstream connections. Such losses, coupled with sensory decline, driving limitations [10], and decreasing social networks [11, 12] undermine both older adults’ ability to prepare for disasters and cope with post-disaster consequences. Specifically, FCT posits that social disparities play a crucial role in shaping both the causes and distribution of health outcomes, and underscores the importance of ensuring equal distribution of resources to mitigate prevailing health risks and render fair access to health advantages across all communities [13, 14].

Consequences of disasters for older adults

When disasters devastate homes, prevent access to social services, and mandate evacuations or relocations, older individuals are rendered particularly vulnerable due to the interruptions such conditions bring to their health care [15]. A recent study found that the cyclones, floods, droughts, and heatwaves in Odisha, India, most affected the vulnerable segments of the population, including people with disabilities and older adults [16]. Comparably, studies conducted following the 2015 Chennai floods found that several older patients living in the affected areas experienced insufficient access to medications for cardiometabolic conditions [17]. Older people in West Bengal’s rural areas and the slums of Kolkata endured extreme temperatures leading to heat exhaustion [18]. Disaster exposure affects older adults’ physical and functional health in myriad ways, including weight gain, fatigue, frailty, and deficits in physical and social activity [19].

Human-made disasters also take a toll on older adults. During political, social, or health crises, older persons experience negative emotions due to social marginalization. COVID-19, for instance, increased instances of ageism. Such instances ranged from disparaging slogans like “Boomer remover” and “stay home” to the well-intentioned but demeaning messages to sequester indefinitely to the more obvious cases of discrimination like age-based rationing of life-saving treatments [20–25]. Such attitudes are often internalized [26], discourage social interactions and activity [27, 28], and may ultimately damage health. As such, compared to their younger peers, older adults have a higher prevalence of anxiety, sadness, and posttraumatic stress disorder (PTSD) in response to human-generated calamities such as pandemics, protests, riots, armed conflicts, and acts of hate and terrorism [29, 30].

Existing studies find adverse health repercussions of natural and human-made disasters among older adults [31, 32]. However, such studies are scarce in LMICs like India where the proportion of older adults is rapidly increasing. As part of preparing all the states and union territories of the country in implementing the National Disaster Management Act, 2005, a comprehensive national plan was developed in India in 2016 [33]. The plan provides a holistic guidance for disaster risk reduction, mitigation, preparedness, response, recovery and rehabilitation, and suggests an optimum preparedness strategy for vulnerable populations including older adults who may be faced with multiple age-related limitations. Paying heed to this, we examine (1) the statewide prevalence of natural and human-made disasters in India and (2) the association between such disasters and poor self-rated health (SRH), difficulty in activities of daily living (ADL) and instrumental activities of daily living (IADL), communicable diseases (CDs), and non-communicable diseases (NCDs), depressive symptoms and psychiatric disorders among older Indians. We expect a positive association between natural or human-made disasters and each of the study outcomes. Fig 1 provides the conceptual framework for our study, grounded in FCT, presenting the direct effects of disasters on adverse health outcomes as and their indirect effects via increased individual, household, and community level vulnerabilities that lead to various psychological stressors which ultimately result in more adverse health outcomes.

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Fig 1. Conceptual framework of the study.

https://doi.org/10.1371/journal.pone.0307371.g001

Methods

Study design and participants

A cross-sectional study design was adopted. The study utilized publicly available secondary data from the Longitudinal Ageing Study in India (LASI- wave 1) conducted during 2017–18, which investigates the health, economic, and social determinants and consequences of population aging in India [34]. The sample in the LASI survey included 72,250 individuals aged 45 and above and their spouses regardless of age across all states and union territories of India except Sikkim. Households with at least one member aged 45 and above were taken as the eventual unit of observation. The data provides scientific evidence on demographics, household economic status, chronic health conditions, symptoms-based health conditions, functional and mental health, biomarkers, health care utilization, work and employment. It enables cross-state analyses and cross-national analyses of aging, health, economic status, and social behavior, and has been designed to evaluate the effect of changing policies and behavioral outcomes in India. Detailed information on the sampling frame is available in the LASI wave-1 Report [34]. After dropping the missing cases on the key variable, the final sample comprised 29,333 older adults (14,120 males and 15,213 females) aged 60 years and above.

During the LASI survey, written informed consent was obtained from all participants and their informants. The Indian Council of Medical Research (ICMR) extended the necessary guidelines and ethics approval for undertaking the LASI survey. All methods were carried out in accordance with the relevant guidelines and regulations of the ICMR.

Outcome variables.

There were seven health outcome variables and all of them were dichotomized in the study. SRH was coded as good which includes very good, good and fair and, poor which includes poor and very poor. SRH is a subjective measure of one’s judgments about the personal health status, is considered the valuable indicator of long-term health status, and predicts illness and disability in the future [35]. ADL is a term used to refer to normal daily self-care activities (such as movement in bed, changing position from sitting to standing, feeding, bathing, dressing, grooming, personal hygiene etc.) The ability or inability to perform ADLs is used to measure a person’s functional status, especially in the case of people with disabilities and the older adults; and difficulty in ADL was coded as yes representing the presence of at least one difficulty. IADL are not necessarily related to the fundamental functioning of a person, but they let an individual live independently in a community. Respondents were asked if they were having any difficulties that were expected to last more than three months, such as preparing a hot meal, shopping for groceries, making a telephone call, taking medications, doing work around the house or garden, managing money (such as paying bills and keeping track of expenses), and getting around or finding an address in unfamiliar places; and difficulty in IADL was coded as yes representing the presence of at least one difficulty.

CDs among older adults were calculated using ten major communicable diseases. The respondent was asked about acute diseases diagnosed by health professionals. Ten diseases included (i) Jaundice/ Hepatitis (ii) Tuberculosis (TB) (iii) Malaria (iv) Diarrhea/gastroenteritis (v) Typhoid (vi) Urinary Tract Infection (vii) Anaemia (viii) Chikungunya (ix) Dengue (x) Others. All the above questions on the scale of two e.g. 0 ‘No’ and 1 ‘Yes’. The scale of 0–10 was obtained by using egen command. The variable was recoded as 1 “Yes” if respondent has any of the diseases and 0 “No” otherwise. NCDs among older adults were assessed using eight chronic diseases diagnosed ever by any health professional. Eight chronic diseases included (i) Hypertension or high blood pressure, (ii) Diabetes or high blood sugar, (iii) Cancer or a malignant tumor, (iv) Chronic lung diseasesa such as asthma, chronic obstructive pulmonary disease/Chronic bronchitis or other chronic lung problems, (v) Chronic heart diseases such as Coronary heart disease (heart attack or Myocardial Infarction), congestive heart failure, or other chronic heart problems, (vi) Stroke, (vii) Arthritis or rheumatism, Osteoporosis or other bone/joint diseases, (viii) High cholesterol. All the above questions on a scale of two, e.g., 0 “No” and 1 “Yes”. The scale of 0–8 was obtained by using egen command. The variable was recoded as 1 “Yes” if the respondent has any of the diseases and 0 “No” otherwise.

Depressive symptoms among older adults were calculated using the Short Form Composite International Diagnostic Interview (CIDI-SF), with a score of 3 or more coded as 1 for “diagnosed with depression” and less than 3 coded as 0 for “not diagnosed with depression”. This scale estimates a probable psychiatric diagnosis of major depression and has been validated in field settings and is widely used in population-based health surveys [36]. Psychiatric disorder was coded as 1 if the respondent has been diagnosed with any neurological or psychiatric disorder (depression, Alzheimer’s disease, dementia, psychiatric problems such as unipolar/bipolar disorder, schizophrenia, neurological problems such as neuropathy, convulsions, migraine, Parkinson’s or others) and 0 otherwise.

Key explanatory variable.

The main explanatory variable was experiencing natural and human-made disaster in the last five years. The question used for natural disasters was, “In the last five years, has your health been severely affected by disasters such as floods, landslides, extreme cold and hot weather, cyclones/typhoons, droughts, earthquakes, tsunamis, or any other natural calamities?” Question used for human-made disaster was “In the last five years, has your health been severely affected by human-made incidents such as riots, terrorism, building collapses, fires, traffic accidents or any other human-made incidents?” It was dichotomized into 0, representing not experiencing any natural or human-made disaster, and 1, representing yes. Any disaster was created using the questions and coding, either natural or human-made, as 1 or 0.

Others explanatory variables.

Individual factors. Age was categorized into groups of 60–69 years, 70–79 years and 80+ years. Sex was categorized as male and female. Educational status was categorized as no education/primary, secondary and higher. Work status was recoded into never worked, ever worked but currently not, and currently working.

Household-related factors. The monthly per capita expenditure (MPCE) quintile was assessed using household consumption data. Sets of 11 and 29 questions on the expenditures on food and non-food items, respectively, were used to canvas the sample households. Food expenditure was collected based on a reference period of seven days, and non-food expenditure was collected based on reference periods of 30 days and 365 days. Food and non-food expenditures have been standardized to the 30-day reference period. The MPCE is computed and used as the summary measure of consumption [34]. The variable was then divided into three quintiles, i.e., poor, middle, and rich. Caste was recoded as Scheduled Caste (SC), Scheduled Tribe (ST), Other Backward Class (OBC), and others. The Scheduled Caste includes a group of population that is socially and economically segregated and deprived. The OBC is the group of people who were identified as “educationally, economically and socially backward”. The “other” caste category is identified as those having higher social status. Religion was coded as Hindu, Muslim, and Others.

Community-related factors. Mean education in PSUs was calculated as average educational level of respondents in each PSU. Mean wealth in PSUs was calculated as the average MPCE quintile of each PSU. Distance to primary healthcare was assessed using the community survey question. Immunization in PSUs included receiving influenza vaccine, pneumococcal vaccine, hepatitis B vaccine, typhoid vaccine, Diphtheria and Tetanus (dT) or any other vaccine. Place of residence was categorized as rural and urban. The regions were coded as North, Central, East, Northeast, West, and South.

Statistical analysis

Descriptive statistics and cross-tabulations were presented in the study. The multivariate logistic regression only allows us to examine the associations at single level and does not account for the variation at another level of nested data. Besides, some locations may be more prone to disasters and there are regional and community-level differences in vulnerability to disasters. Therefore, considering the hierarchical structure of LASI data, we used multivariate multilevel logistic regression analysis to examine the association between experience of disaster and various health outcomes (poor SRH, difficulty in ADL, difficulty in IADL, CDs, NCDs, depressive symptoms and psychiatric disorders).

Initially, we conducted fixed-effects multilevel logistic regression analysis, considering individuals (level 1) nested within households (level 2) nested within community (level 3). However, we encountered convergence difficulties during this process. One possible explanation for the lack of convergence in the estimates from the three-level multilevel model is the smaller sample size of household-level data, which reduced statistical power and increased the uncertainty of the estimates. Analyzing the distribution of household members in the data, we observed that the majority of households consisted of only one or two individuals (as LASI collected data mainly from older members and their spouses), with a small proportion having three or more individuals. Such a distribution introduces significant variability or heterogeneity in the household-level data, making it more challenging to estimate the parameters and achieve a stable solution accurately. Therefore, we only performed a two-level (individual and community-levels) random intercept logistic regression model for the current analysis (using melogit command in Stata). We included all the individual (age, sex, education and work status), household (wealth quintiles, caste and religion) and community characteristics (mean education, mean wealth, distance to primary healthcare, and immunization in PSUs) in the model. In a random intercept multilevel logistic regression model, we include an effect for each area that impacts on all individuals in that area equally, regardless of their background characterictics.

In the above equation, y_ij is our dependent or response variable: the outcome for individual i living in PSU j, the chances of experiencing disaster. The respondents are numbered from i = 1, …, N and each lives in one PSU j = 1, …, J. There are n_j respondents in PSU j so N = ∑ ^J_{j = 1} n_j. Further, x_pij are the independent or explanatory variables, again measured on individual i in PSU j. The subscript p is used simply to distinguish between the different variables; for example x_1ij might be the individual’s age in years and x_2ij a dummy variable indicating the subject’s sex (0 = male, 1 = female). x_pj are also independent variables, but these are measured at the contextual or PSU level; that is, they take the same value for all individuals living in PSU j. These variables may be directly observed or measured at the PSU level; for example, x_3j may be the distance to primary healthcare in PSU j. Alternatively, the contextual variables may represent an aggregation of individual measures; x_4j may be the mean education of the respondents in PSU j.

β_p is the regression coefficient associated with x_pij or x_pj. So β₁ would indicate the average change in disaster associated with a 1-year increase in age and β₂ would show the average effect of being female on experiencing disaster (relative to that for the baseline category, male, for which x_2ij = 0). u_0j is the estimated effect or residual for area j. This is the difference that we expect to see in experiencing disaster for an individual in PSU j compared to an individual in the average PSU, after taking into account those (individual, household and community) characteristics that have been included in the model. The 0 in the subscript denotes that this is a random intercept residual, a departure from the overall intercept β₀ applying equally to everyone in PSU j regardless of individual’s characteristics. Such a model can offer a comprehensive framework for understanding the ways in which places or community level factors can affect the health outcomes associated with disaster among older adults, especially when the disaster experiences may vary across communities. While adjusting for such contextual effects, the intra-class correlation coefficients (ICC) are computed to quantify the amount of design effect present.

The models are diagnosed using the Log-likelihood Ratio test (LR test), Akaike’s Information Criterion (AIC) and Bayesian information criterion (BIC), and the results from the model diagnostics including ICC are presented in S1 Table. Stata version 15 has been used throughout the analysis [37]. The individual weights were used for computing the estimates to account for complex survey design and to make the estimates nationally representative.

Multivariate multilevel analysis fit statistics.

ICC, LR test, AIC and BIC were calculated for the appropriateness of multilevel logistic regression and checking the presence of clustering. To choose the appropriate model for the current study, we first fit the null model to examine the between community variation and justify the use of multilevel analysis and then a model were built with dependent variable and individual level factors. Accordingly, the measures of community variation (obtained from random-intercept model) were estimated as the ICC and the value was significant in case of all the health outcomes considered in this study. We used ICC value greater than 5% to consider a variation of natural and human-made disasters across the clusters (S1 Table).

Results

Socio-demographic profile and state-wise prevalence of natural and/or human-made disaster in the study sample

Table 1 presents the socio-economic profile of older adults in India. About 11% of older adults were from age group 80 years and above. The mean age of the study population was 69.2 years. Nearly 74% of the older adults had no education or were educated till primary. Almost 35% of the older adults were currently not working whereas about 9% were retired. Mean years of education in the PSU was 3.4 years and mean wealth quintile of the PSUs was 1.5. Mean distance from to primary health care centre in PSUs were 2.3 km. Around 6.42% were immunized and 28.78% were urban residents.

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Table 1. Socio-economic profile of the sample population.

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Table 2 presents the state-wise prevalence of natural and human-made disasters that affected the health of older individuals, LASI, wave-1. Overall, 3.58% of older adults reported that they have encountered any type of natural or human-made disaster whereas, 2.70% reported some type of natural disaster and 1.07% reported some type of human-made disaster in the past five years. The prevalence of natural disasters that affected the health of the older individuals was highest in Bihar (6.69%) followed by Madhya Pradesh (5.42%) and Uttar Pradesh (5.99%). Moreover, the prevalence of human-made disasters that affected the health of the older individuals was highest in Jammu and Kashmir (4.30%) followed by Madhya Pradesh (2.45%) and Bihar (1.89%).

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Table 2. State-wise prevalence of natural, human-made and any disaster.

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Natural and/or human-made disaster and associated health outcomes among older adults

Table 3 presents the prevalence of health outcomes of older individuals who experienced / did not experience natural and human-made disaster. Compared to those who did not experience any (natural or human-made) disaster, older adults who experienced any disaster had a higher prevalence of poor SRH (33.4% vs 23.31%), difficulty in ADL (33.94% vs 23.00%), difficulty in IADL (60.09% vs 47.70%), CDs (49.57% vs 25.86%), depressive symptoms (17.30% vs 8.06%) and psychiatric disorders (3.42% vs 2.78%).

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Table 3. Prevalence of different health outcomes among those who experienced/ did not experience natural, human-made and any disaster.

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Table 4 presents the multivariate random intercept multilevel logistic regression estimates of health outcomes affected by natural and human-made disaster among older adults. The odds of poor SRH were significantly higher among older adults who experienced natural disaster [AOR: 1.51; CI: 1.26–1.81] or human-made disaster [AOR: 2.19; CI: 1.70–2.82] in comparison to older adults who did not experience natural or human-made disaster, respectively. The odds of difficulty in ADL and IADL were significantly higher among older adults who experienced natural disaster [AOR: 1.78; CI: 1.47–2.14 and AOR: 1.58; CI: 1.32–1.88] or human-made disaster [AOR: 2.18; CI: 1.68–2.84 and AOR: 1.79; CI: 1.39–2.31] in comparison to those who did not experience natural or human-made disaster. The odds of CDs and NCDs were significantly higher among older adults who suffered from any natural disaster [AOR: 2.37; CI: 2.00–2.82 and AOR: 1.44; CI: 1.22–1.70] or human-made disaster [AOR: 1.65; CI: 1.28–2.12 and AOR: 1.34; CI: 1.05–1.71] in comparison to those who did not experience natural or human-made disaster. The odds of depressive symptoms and psychiatric disorder were significantly higher among older adults who experienced natural disaster [AOR: 1.60; CI: 1.26–2.03 and AOR: 1.45; CI: 1.01–2.18] or human-made disaster [AOR: 1.89; CI: 1.33–2.67 and AOR: 2.07; CI: 1.24–3.47] in comparison to those who did not experience natural or human-made disaster, respectively.

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Table 4. Multivariate multilevel logistic regression estimates of health outcomes by natural, human-made and any disaster.

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Table 4 also presents the multivariate multilevel logistic regression estimates of health outcomes associated with any (natural or human-made) disaster among older adults. After accounting for various individual-, household-, and community variables, the estimates suggest that older adults who experienced any disaster were associated with poor SRH [AOR: 1.64; CI: 1.40, 1.92], difficulty in ADL and IADL [AOR: 1.89; CI: 1.61, 2.21 and AOR: 1.63; CI: 1.40, 1.89], CDs and NCDs [AOR: 2.12; CI: 1.83, 2.46 and AOR: 1.38; CI: 1.20, 1.60], depressive symptoms and psychiatric disorder [AOR: 1.67; CI: 1.55, 2.05 and AOR: 1.52; CI: 1.33, 2.18]. S2 Table presents the results from the multivariate multilevel logistic regression analyses of health outcomes by natural and/or human-made disasters and by individual-, household-, and community-level characteristics of older adults.

Discussion

We assessed the prevalence of natural and human-made disasters and the association between such disasters and SRH, functional limitations, CDs and NCDs, depression symptoms and, psychiatric disorders among older adults in India. We found that about 4% of older Indians had experienced some natural or human-made disaster within the past five years. Importantly, the experience of these disasters was significantly linked to adverse physical, mental, and functional health outcomes among older Indians. Even after considering several individual, household, and community-related characteristics, we find that the risk of major depression among older Indians who had faced natural or human-made disasters was nearly double that of those who had not been exposed. These findings are consistent with previous studies [38–41], which also have highlighted the profound impact of disasters on mental health. The estimated prevalence of major depression among disaster-affected individuals in this study was nearly 18%. In other studies, the prevalence of major depression among survivors of the earthquake in Taiwan was 6.4% [42], while in Turkey, it was 11.0% [43], which is comparatively lower than that observed in our study. Bandla et al. (2019) reported that the depression prevalence in people living in flood-affected areas in Tamil Nadu, India, was 45%, which is relatively higher than in our study [44]. These differences may partially be attributed to the use of different diagnostic tools for major depression.

Research on the psychopathological consequences of disasters in older adults is gaining traction given the growing aging population exposed to such events. Disasters exert long-term effects, including lingering mental distress. Human-made disasters may affect individuals psychologically up to 6–14 years after the disaster, while coping with natural disasters can take up to 3 years after the event [45]. Thirty-seven years after the Tangshan earthquake in Japan, exposed persons were at greater risk of depression [46]. Severe earthquakes can lead to as deaths of kin and non-kin, triggering both acute and chronic mental distress [47]. Disaster related distress, however, varies across age. According to FCT, older adults may be particularly vulnerable to mental distress in the aftermath of disasters because of factors such as reduced capacity to mobilize assistance and a heightened sense of insecurity stemming from personal, social, and physical losses [48, 49]. Therefore, FCT underscores the importance of addressing social inequalities and improving SES to mitigate the adverse mental health effects of disasters among older adults. By reducing discrimination and improving SES, communities can better promote health and well-being by addressing both the individual and contextual factors that contribute to mental illness prevention [50].

In addition to psychological distress, we found that individuals who faced natural or human-made disasters were more likely to report functional deficits, as measured using ADLs and IADLs. Disasters increase the vulnerability of older adults to functional disabilities [51, 52]. This increased vulnerability to functional constraints among disaster survivors is consistent with FCT, which suggests that disasters worsen pre-existing social inequities and raise the risk of adverse health outcomes. For instance, and consistent with our findings, a cross-sectional study on the aftermath of the Bam earthquake in Iran shows that survivors report greater ADL limitations than non-exposed peers five years after the disaster [53]. Similarly, research conducted in China following the Wenchuan earthquake revealed decreased ADL capacity and muscle force among survivors than peers who remained unexposed [54]. Disasters often disrupt social interactions, such as separation from family and friends due to residential relocation, which can negatively impact functional health [55]. As expected, in our study, the survivors of natural or human-made disasters reported a greater likelihood of poor SRH than the non-exposed individuals. These findings align with previous research [56, 57] indicating a significant independent association between functional and mental health with poor SRH [58]. The potential mechanisms linking disasters and poor SRH in survivors, as evident in previous studies, include a various stressors, like post-disaster economic deprivation [59], breakdown of social networks [60], and reduced mental and physical capacities [61], underscoring the complex interplay between social conditions and health outcomes in disaster-affected populations.

Disaster survivors in our study also were at greater risk of CDs and NCDs. Previous literature corroborates our findings, indicating that individuals exposed to major disasters remain susceptible to developing comorbid conditions, such as hypertension, cancer, stroke, diabetes, and chronic lung disease [62]. A recent systematic review highlighted a greater prevalence of chronic conditions, including diabetes mellitus and hypertension, among individuals exposed to disasters such as storms, earthquakes, and major terrorist events like the World Trade Center Tower (WTCT) attack [63]. Exposure to polluted air and toxic chemicals during the WTCT attack has been linked to neurological conditions, certain types of cancer, and respiratory conditions such as asthma, chronic obstructive lung disease, pulmonary fibrosis, and pneumonia [64]. Disaster survivors often suffer from a multitute of exacerbated CDs and NCDs, likely attributable to adverse post-disaster conditions, like compromised sanitation, inadequate nutrition, and contaminated drinking water, temperature extremes, rise in infections, and disrupted health services [65]. For instance, a study conducted in Indonesia in 1992–93 revealed a significant increase in the risk of diarrheal illnesses, including paratyphoid fever, following the flooding [66]. Similarly, the South Asia earthquake in Pakistan in 2005 triggered multiple contiguous clusters of measles [67], underscoring the increased susceptibility to infectious diseases among those affected by disasters. These findings highlight the need for comprehensive public interventions targeted at both acute health needs and long-term health outcomes in disaster-prone communities.

Policy implications

Both natural and human-made calamities exert disastrous health impacts on older adults. Considering FCT, which underscores the role of social inequities in shaping health outcomes, one way to improve both disaster preparedness and post-disaster management is to actively engage older adults as stakeholders in disaster preparedness and management [68]. Instead of victimizing older adults, programs and policies should prioritize promoting healthy aging, and invest in developing age-friendly communities. By fostering environments that support healthy aging, such initiatives not only build individual resilience but also empower older adults to confront disasters with greater self-confidence and facilitate post-disaster coping [68]. Furthermore, strengthening health care systems is crucial in mitigating the impact of disasters on older adults. Stronger health care systems would increase the capacity to manage the unexpected influx of older patients during disasters, ensuring timely and effective care delivery.

Conclusions

This study underscores the increased vulnerability of older survivors of natural or human-made disasters within the past five years to physical and mental health challenges. Specifically, compared to their non-exposed counterparts, older survivors of natural or human-made disasters endure an inflated risk of SRH, functional difficulties, chronic CDs and NCDs, depressive symptoms, and psychiatric disorders. These findings call for a comprehensive, age-sensitive approach to interventions aimed at restoring the physical, mental and psychosocial well-being of survivors in the aftermath of a disaster. With age, people spend more time in their immediate neighborhoods and become increasingly dependent on local resources and services. As such, on a policy and intervention front, there is a need for more local and community-based disaster preparedness, improved warning systems and coordinated disaster responses, including rescue, relief, and accessible shelter options, especially in areas with substantial older adult populations.

Strengths and limitations

As far as we know, this is the first study to assess the relevance of natural versus human-made disasters for such a wide variety of physical, functional, and mental health outcomes among older Indians, and we did so by employing a sizeable nationally representative sample of older adults. Moreover, we used standardized and validated measures, adapted from the US based Health and Retirement Study, for assessing outcomes of physical and psychological health. That said, our study has several limitations. First, our key explanatory variable, the experience of disasters, is assessed retrospectively, therefore prone to recall bias and misreporting. Second, the study’s cross-sectional nature prevents us from rendering any causal or temporal inferences about disaster exposure, and its health implications. Future studies using upcoming waves of LASI may be able to offer a more clarifying image of the relationship between disaster experience and health. Because some health concerns may not emerge immediately after exposure but instead take several years to surface [64], a health trajectories approach may offer greater opportunities for early intervention to maintain physical and mental potency among older disaster survivors. Finally, the inherent bias in the self-reporting of some of the outcome variables such as CDs and NCDs and SRH may have influenced the current results.

Supporting information

S1 Table. Model diagnostics of the multivariate multilevel logistic regression analyses of health outcomes by natural and/or human-made disasters among older adults.

https://doi.org/10.1371/journal.pone.0307371.s001

(DOCX)

S2 Table. Multivariate multilevel logistic regression analyses of health outcomes by natural or human-made disasters and by background characteristics of older adults.

https://doi.org/10.1371/journal.pone.0307371.s002

(DOCX)

References

1. World Health Organization. Health emergency and disaster risk management framework. Geneva: World Health Organization, https://iris.who.int/handle/10665/326106 (2019, accessed 31 March 2024).
2. National Portal of India, https://www.india.gov.in/ (accessed 2 November 2021).
3. Kumar KA, Walia A, Chaturvedi S. India disaster report. National Institute of Disaster Management, IIPA Campus, New Delhi, http://nidm.gov.in/PDF/Pubs/India%20Disaster%20Report%202011.Pdf (2012, accessed 16 March 2024).
- View Article
- Google Scholar
4. Kar N. Indian research on disaster and mental health. Indian J Psychiatry 2010; 52: S286–S290. pmid:21836696
- View Article
- PubMed/NCBI
- Google Scholar
5. Mohandas E. Roadmap to Indian Psychiatry. Indian J Psychiatry 2009; 51: 173–179. pmid:19881044
- View Article
- PubMed/NCBI
- Google Scholar
6. Klinenberg E. Heat Wave: A Social Autopsy of Disaster in Chicago. Chicago, IL: University of Chicago Press, https://press.uchicago.edu/ucp/books/book/chicago/H/bo20809880.html (2015, accessed 10 May 2023).
7. Malik S, Lee DC, Doran KM, et al. Vulnerability of older adults in disasters: emergency department utilization by geriatric patients after Hurricane Sandy. Disaster medicine and public health preparedness 2018; 12: 184–193. pmid:28766475
- View Article
- PubMed/NCBI
- Google Scholar
8. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav 1995; Spec No: 80–94. pmid:7560851
- View Article
- PubMed/NCBI
- Google Scholar
9. Hatzenbuehler ML, Phelan JC, Link BG. Stigma as a Fundamental Cause of Population Health Inequalities. Am J Public Health 2013; 103: 813–821. pmid:23488505
- View Article
- PubMed/NCBI
- Google Scholar
10. Pinto JM, Kern DW, Wroblewski KE, et al. Sensory Function: Insights From Wave 2 of the National Social Life, Health, and Aging Project. The Journals of Gerontology: Series B 2014; 69: S144–S153. pmid:25360015
- View Article
- PubMed/NCBI
- Google Scholar
11. Holt-Lunstad J. Why Social Relationships Are Important for Physical Health: A Systems Approach to Understanding and Modifying Risk and Protection. Annu Rev Psychol 2018; 69: 437–458. pmid:29035688
- View Article
- PubMed/NCBI
- Google Scholar
12. Wrzus C, Hänel M, Wagner J, et al. Social network changes and life events across the life span: a meta-analysis. Psychol Bull 2013; 139: 53–80. pmid:22642230
- View Article
- PubMed/NCBI
- Google Scholar
13. Clouston SAP, Link BG. A Retrospective on Fundamental Cause Theory: State of the Literature and Goals for the Future. Annu Rev Sociol 2021; 47: 131–156. pmid:34949900
- View Article
- PubMed/NCBI
- Google Scholar
14. Mackenbach JP, Kulhánová I, Bopp M, et al. Variations in the relation between education and cause-specific mortality in 19 European populations: a test of the “fundamental causes” theory of social inequalities in health. Social Science & Medicine 2015; 127: 51–62.
- View Article
- Google Scholar
15. Bell SA, Horowitz J, Iwashyna TJ. Health Outcomes After Disaster for Older Adults With Chronic Disease: A Systematic Review. Gerontologist 2019; 60: e535–e547.
- View Article
- Google Scholar
16. Patel SK, Mathew B, Nanda A, et al. Voices of rural people: Community-level assessment of effects and resilience to natural disasters in Odisha, India. IJPS 2020; 6: 3–15.
- View Article
- Google Scholar
17. The Chennai floods of 2015 and the health system response. Indian Journal of Medical Ethics, http://ijme.in/articles/the-chennai-floods-of-2015-and-the-health-system-response/?galley=html (accessed 3 October 2023).
18. Dasgupta A, Bandyopadhyay S, Bandyopadhyay L, et al. How frail are our elderly? An assessment with Tilburg frailty indicator (TFI) in a rural elderly population of West Bengal. J Family Med Prim Care 2019; 8: 2242–2248. pmid:31463237
- View Article
- PubMed/NCBI
- Google Scholar
19. Bell SA, Choi H, Langa KM, et al. Health risk behaviors after disaster exposure among older adults. Prehospital and disaster medicine 2019; 34: 95–97. pmid:30642407
- View Article
- PubMed/NCBI
- Google Scholar
20. Cesari M, Proietti M. COVID-19 in Italy: Ageism and Decision Making in a Pandemic. J Am Med Dir Assoc 2020; 21: 576–577. pmid:32334771
- View Article
- PubMed/NCBI
- Google Scholar
21. Cohn-Schwartz E, Ayalon L. Societal Views of Older Adults as Vulnerable and a Burden to Society During the COVID-19 Outbreak: Results From an Israeli Nationally Representative Sample. J Gerontol B Psychol Sci Soc Sci 2021; 76: e313–e317. pmid:32882025
- View Article
- PubMed/NCBI
- Google Scholar
22. Kessler E-M, Bowen CE. COVID ageism as a public mental health concern. The Lancet Healthy Longevity 2020; 1: e12. pmid:34173613
- View Article
- PubMed/NCBI
- Google Scholar
23. Lichtenstein B. From ‘Coffin Dodger’ to ‘Boomer Remover’: Outbreaks of Ageism in Three Countries With Divergent Approaches to Coronavirus Control. J Gerontol B Psychol Sci Soc Sci 2021; 76: e206–e212. pmid:32719851
- View Article
- PubMed/NCBI
- Google Scholar
24. Meisner BA. Are you OK, Boomer? Intensification of ageism and intergenerational tensions on social media amid COVID-19. Leisure Sciences 2020; 43: 56–61.
- View Article
- Google Scholar
25. Previtali F, Allen LD, Varlamova M. Not Only Virus Spread: The Diffusion of Ageism during the Outbreak of COVID-19. J Aging Soc Policy 2020; 32: 506–514. pmid:32507060
- View Article
- PubMed/NCBI
- Google Scholar
26. Cabin S, Jardin E. Why Preventing Ageist Attitudes Is Not Enough during COVID-19 Pandemic. Health Soc Work 2021; hlab008. pmid:33956967
- View Article
- PubMed/NCBI
- Google Scholar
27. Kang H, Kim H. Ageism and Psychological Well-Being Among Older Adults: A Systematic Review. Gerontol Geriatr Med 2022; 8: 23337214221087023. pmid:35434202
- View Article
- PubMed/NCBI
- Google Scholar
28. Rothermund K, Klusmann V, Zacher H. Age Discrimination in the Context of Motivation and Healthy Aging. J Gerontol B Psychol Sci Soc Sci 2021; 76: S167–S180. pmid:34515776
- View Article
- PubMed/NCBI
- Google Scholar
29. Lai DWL, Liu EHS, Yan ECW, et al. Exposure to socio-political unrest and wellbeing of older people in Hong Kong. BMC Geriatr 2022; 22: 768. pmid:36151516
- View Article
- PubMed/NCBI
- Google Scholar
30. Charlson F, van Ommeren M, Flaxman A, et al. New WHO prevalence estimates of mental disorders in conflict settings: a systematic review and meta-analysis. Lancet 2019; 394: 240–248. pmid:31200992
- View Article
- PubMed/NCBI
- Google Scholar
31. Bodas M, Peleg K, Stolero N, et al. Risk perception of natural and human-made disasters—cross sectional study in eight countries in Europe and beyond. Frontiers in public health 2022; 10: 825985. pmid:35252099
- View Article
- PubMed/NCBI
- Google Scholar
32. De Rubeis V, Lee J, Anwer MS, et al. Impact of disasters, including pandemics, on cardiometabolic outcomes across the life-course: a systematic review. BMJ open 2021; 11: e047152. pmid:33941635
- View Article
- PubMed/NCBI
- Google Scholar
33. National Disaster Management Authority. National Disaster Management Plan: A comprehensive step towards reducing disaster risk ‐ India. Ministry of Home Affairs, GoI, https://reliefweb.int/report/india/national-disaster-management-plan-comprehensive-step-towards-reducing-disaster-risk (04 2021, accessed 31 March 2024).
34. International Institute for Population Sciences (IIPS) NP for, Health Care of Elderly (NPHCE), MoHFW HTHCS of, (USC) PH (HSPH) and the U of SC. Longitudinal Ageing Study in India (LASI) Wave 1, 2017–18, India Report. 2020.
35. Idler EL, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community studies. Journal of health and social behavior 1997; 21–37. pmid:9097506
- View Article
- PubMed/NCBI
- Google Scholar
36. Muhammad T, Meher T, Sekher TV. Association of elder abuse, crime victimhood and perceived neighbourhood safety with major depression among older adults in India: a cross-sectional study using data from the LASI baseline survey (2017–2018). BMJ open 2021; 11: e055625. pmid:34907072
- View Article
- PubMed/NCBI
- Google Scholar
37. StataCorp LP. Stata 15 Base Reference Manual. College Station, TX, StataCorp LLC, https://www.merit.unu.edu/wp-content/docs/stata/%5BM%5DMATAReferenceManual_Release13.pdf (2017, accessed 25 March 2024).
38. Agyapong VI, Ritchie A, Brown MR, et al. Long-term mental health effects of a devastating wildfire are amplified by socio-demographic and clinical antecedents in elementary and high school staff. Frontiers in psychiatry 2020; 11: 448. pmid:32528323
- View Article
- PubMed/NCBI
- Google Scholar
39. Agyapong VI, Juhas M, Omege J, et al. Prevalence rates and correlates of likely post-traumatic stress disorder in residents of fort mcmurray 6 months after a wildfire. International Journal of Mental Health and Addiction 2021; 19: 632–650.
- View Article
- Google Scholar
40. Wilson-Genderson M, Heid AR, Pruchno R. Onset of multiple chronic conditions and depressive symptoms: a life events perspective. Innovation in aging 2017; 1: igx022. pmid:30480117
- View Article
- PubMed/NCBI
- Google Scholar
41. Tortella-Feliu M, Fullana MA, Pérez-Vigil A, et al. Risk factors for posttraumatic stress disorder: An umbrella review of systematic reviews and meta-analyses. Neuroscience & Biobehavioral Reviews 2019; 107: 154–165.
- View Article
- Google Scholar
42. Wu H-C, Chou P, Huang-Chih Chou F, et al. Survey of quality of life and related risk factors for a Taiwanese village population 3 years post-earthquake. Australian & New Zealand Journal of Psychiatry 2006; 40: 355–361.
- View Article
- Google Scholar
43. Kılıc C, Aydın İ, Taşkıntuna N, et al. Predictors of psychological distress in survivors of the 1999 earthquakes in Turkey: effects of relocation after the disaster. Acta Psychiatr Scand 2006; 114: 194–202. pmid:16889590
- View Article
- PubMed/NCBI
- Google Scholar
44. Bandla S, Nappinnai NR, Gopalasamy S. Psychiatric morbidity in December 2015 flood-affected population in Tamil Nadu, India. International journal of social psychiatry 2019; 65: 338–344. pmid:31068043
- View Article
- PubMed/NCBI
- Google Scholar
45. Thomas CR. Psychiatric Sequelae of Disasters. Journal of Burn Care & Research 2006; 27: 600–605. pmid:16998391
- View Article
- PubMed/NCBI
- Google Scholar
46. Gao X, Leng Y, Guo Y, et al. Association between earthquake experience and depression 37 years after the Tangshan earthquake: a cross-sectional study. BMJ open 2019; 9: e026110. pmid:31434762
- View Article
- PubMed/NCBI
- Google Scholar
47. Arnberg FK, Eriksson N-G, Hultman CM, et al. Traumatic bereavement, acute dissociation, and posttraumatic stress: 14 years after the MS Estonia disaster. Journal of traumatic stress 2011; 24: 183–190. pmid:21442665
- View Article
- PubMed/NCBI
- Google Scholar
48. Yang YK, Yeh TL, Chen CC, et al. Psychiatric morbidity and posttraumatic symptoms among earthquake victims in primary care clinics. General Hospital Psychiatry 2003; 25: 253–261. pmid:12850657
- View Article
- PubMed/NCBI
- Google Scholar
49. Ticehurst S, Webster RA, Carr VJ, et al. The Psychosocial Impact of an Earthquake on the Elderly. International Journal of Geriatric Psychiatry 1996; 11: 943–951.
- View Article
- Google Scholar
50. Khan M, Ilcisin M, Saxton K. Multifactorial discrimination as a fundamental cause of mental health inequities. Int J Equity Health 2017; 16: 43. pmid:28257630
- View Article
- PubMed/NCBI
- Google Scholar
51. Dyer CB, Regev M, Burnett J, et al. SWiFT: a rapid triage tool for vulnerable older adults in disaster situations. Disaster medicine and public health preparedness 2008; 2: S45–S50. pmid:18769267
- View Article
- PubMed/NCBI
- Google Scholar
52. Kuroda Y, Iwasa H, Orui M, et al. Risk factor for incident functional disability and the effect of a preventive exercise program: a 4-year prospective cohort study of older survivors from the great East Japan earthquake and nuclear disaster. International journal of environmental research and public health 2018; 15: 1430. pmid:29986471
- View Article
- PubMed/NCBI
- Google Scholar
53. Ardalan A, Mazaheri M, Vanrooyen M, et al. Post-disaster quality of life among older survivors five years after the Bam earthquake: implications for recovery policy. Ageing & Society 2011; 31: 179–196.
- View Article
- Google Scholar
54. Zhang J-L, He H-C, Lin H-D, et al. Motor function and activities of daily living capacity of patients with fractures sustained during the Wenchuan earthquake. Chinese medical journal 2011; 124: 1504–7. pmid:21740806
- View Article
- PubMed/NCBI
- Google Scholar
55. Courtin E, Knapp M. Social isolation, loneliness and health in old age: a scoping review. Health & social care in the community 2017; 25: 799–812. pmid:26712585
- View Article
- PubMed/NCBI
- Google Scholar
56. Conrad KA, Amstadter AB, McCauley JL, et al. Examination of general health following typhoon Xangsane: A pre–postanalysis. International Perspectives in Psychology: Research, Practice, Consultation 2011; 1: 28.
- View Article
- Google Scholar
57. Sannabe A, Aida J, Wada Y, et al. On the direct and indirect effects of the Great East Japan earthquake on self rated health through social connections: Mediation analysis. Japan and the World Economy 2020; 56: 101039. pmid:35814635
- View Article
- PubMed/NCBI
- Google Scholar
58. Hu Y-N, Hu G-C, Hsu C-Y, et al. Assessment of individual activities of daily living and its association with self-rated health in elderly people of Taiwan. International Journal of Gerontology 2012; 6: 117–121.
- View Article
- Google Scholar
59. Demirchyan A, Petrosyan V, Armenian HK, et al. Prospective study of predictors of poor self-rated health in a 23-year cohort of earthquake survivors in Armenia. Journal of epidemiology and global health 2015; 5: 265–274. pmid:26231402
- View Article
- PubMed/NCBI
- Google Scholar
60. Caetano SC, Silva CM, Vettore MV. Gender differences in the association of perceived social support and social network with self-rated health status among older adults: A population-based study in Brazil. BMC Geriatrics; 13. Epub ahead of print 2013. pmid:24229389
- View Article
- PubMed/NCBI
- Google Scholar
61. Lemes JS, Pagotto V, Rodrigues PKA, et al. Association between health self-assessment and types of activities of daily living in older adults. Cadernos Saúde Coletiva 2021; 29: 251–259.
- View Article
- Google Scholar
62. Miller AC, Arquilla B. Chronic diseases and natural hazards: impact of disasters on diabetic, renal, and cardiac patients. Prehospital and disaster medicine 2008; 23: 185–194. pmid:18557300
- View Article
- PubMed/NCBI
- Google Scholar
63. Gohardehi F, Seyedin H, Moslehi S. Prevalence rate of diabetes and hypertension in disaster-exposed populations: a systematic review and meta-analysis. Ethiopian Journal of Health Sciences; 30. pmid:32874087
- View Article
- PubMed/NCBI
- Google Scholar
64. Brackbill RM, Graber JM, Robison WA (Allen). Editorial for “Long-Term Health Effects of the 9/11 Disaster” in International Journal of Environmental Research and Public Health, 2019. Int J Environ Res Public Health 2019; 16: 3289.
- View Article
- Google Scholar
65. Ligon BL. Infectious diseases that pose specific challenges after natural disasters: a review. In: Seminars in pediatric infectious diseases. Elsevier, 2006, pp. 36–45. pmid:16522504
- View Article
- PubMed/NCBI
- Google Scholar
66. Vollaard AM, Ali S, van Asten HA, et al. Risk factors for typhoid and paratyphoid fever in Jakarta, Indonesia. Jama 2004; 291: 2607–2615. pmid:15173152
- View Article
- PubMed/NCBI
- Google Scholar
67. Watson JT, Gayer M, Connolly MA. Epidemics after natural disasters. Emerging infectious diseases 2007; 13: 1. pmid:17370508
- View Article
- PubMed/NCBI
- Google Scholar
68. Phraknoi N, Sutanto J, Hu Y, et al. Older people’s needs in urban disaster response: A systematic literature review. International Journal of Disaster Risk Reduction 2023; 96: 103809.
- View Article
- Google Scholar

[ref1] 1. World Health Organization. Health emergency and disaster risk management framework. Geneva: World Health Organization, https://iris.who.int/handle/10665/326106 (2019, accessed 31 March 2024).

[ref2] 2. National Portal of India, https://www.india.gov.in/ (accessed 2 November 2021).

[ref3] 3. Kumar KA, Walia A, Chaturvedi S. India disaster report. National Institute of Disaster Management, IIPA Campus, New Delhi, http://nidm.gov.in/PDF/Pubs/India%20Disaster%20Report%202011.Pdf (2012, accessed 16 March 2024).
View Article
Google Scholar

[4] View Article

[5] Google Scholar

[ref4] 4. Kar N. Indian research on disaster and mental health. Indian J Psychiatry 2010; 52: S286–S290. pmid:21836696
View Article
PubMed/NCBI
Google Scholar

[7] View Article

[8] PubMed/NCBI

[9] Google Scholar

[ref5] 5. Mohandas E. Roadmap to Indian Psychiatry. Indian J Psychiatry 2009; 51: 173–179. pmid:19881044
View Article
PubMed/NCBI
Google Scholar

[11] View Article

[12] PubMed/NCBI

[13] Google Scholar

[ref6] 6. Klinenberg E. Heat Wave: A Social Autopsy of Disaster in Chicago. Chicago, IL: University of Chicago Press, https://press.uchicago.edu/ucp/books/book/chicago/H/bo20809880.html (2015, accessed 10 May 2023).

[ref7] 7. Malik S, Lee DC, Doran KM, et al. Vulnerability of older adults in disasters: emergency department utilization by geriatric patients after Hurricane Sandy. Disaster medicine and public health preparedness 2018; 12: 184–193. pmid:28766475
View Article
PubMed/NCBI
Google Scholar

[16] View Article

[17] PubMed/NCBI

[18] Google Scholar

[ref8] 8. Link BG, Phelan J. Social conditions as fundamental causes of disease. J Health Soc Behav 1995; Spec No: 80–94. pmid:7560851
View Article
PubMed/NCBI
Google Scholar

[20] View Article

[21] PubMed/NCBI

[22] Google Scholar

[ref9] 9. Hatzenbuehler ML, Phelan JC, Link BG. Stigma as a Fundamental Cause of Population Health Inequalities. Am J Public Health 2013; 103: 813–821. pmid:23488505
View Article
PubMed/NCBI
Google Scholar

[24] View Article

[25] PubMed/NCBI

[26] Google Scholar

[ref10] 10. Pinto JM, Kern DW, Wroblewski KE, et al. Sensory Function: Insights From Wave 2 of the National Social Life, Health, and Aging Project. The Journals of Gerontology: Series B 2014; 69: S144–S153. pmid:25360015
View Article
PubMed/NCBI
Google Scholar

[28] View Article

[29] PubMed/NCBI

[30] Google Scholar

[ref11] 11. Holt-Lunstad J. Why Social Relationships Are Important for Physical Health: A Systems Approach to Understanding and Modifying Risk and Protection. Annu Rev Psychol 2018; 69: 437–458. pmid:29035688
View Article
PubMed/NCBI
Google Scholar

[32] View Article

[33] PubMed/NCBI

[34] Google Scholar

[ref12] 12. Wrzus C, Hänel M, Wagner J, et al. Social network changes and life events across the life span: a meta-analysis. Psychol Bull 2013; 139: 53–80. pmid:22642230
View Article
PubMed/NCBI
Google Scholar

[36] View Article

[37] PubMed/NCBI

[38] Google Scholar

[ref13] 13. Clouston SAP, Link BG. A Retrospective on Fundamental Cause Theory: State of the Literature and Goals for the Future. Annu Rev Sociol 2021; 47: 131–156. pmid:34949900
View Article
PubMed/NCBI
Google Scholar

[40] View Article

[41] PubMed/NCBI

[42] Google Scholar

[ref14] 14. Mackenbach JP, Kulhánová I, Bopp M, et al. Variations in the relation between education and cause-specific mortality in 19 European populations: a test of the “fundamental causes” theory of social inequalities in health. Social Science & Medicine 2015; 127: 51–62.
View Article
Google Scholar

[44] View Article

[45] Google Scholar

[ref15] 15. Bell SA, Horowitz J, Iwashyna TJ. Health Outcomes After Disaster for Older Adults With Chronic Disease: A Systematic Review. Gerontologist 2019; 60: e535–e547.
View Article
Google Scholar

[47] View Article

[48] Google Scholar

[ref16] 16. Patel SK, Mathew B, Nanda A, et al. Voices of rural people: Community-level assessment of effects and resilience to natural disasters in Odisha, India. IJPS 2020; 6: 3–15.
View Article
Google Scholar

[50] View Article

[51] Google Scholar

[ref17] 17. The Chennai floods of 2015 and the health system response. Indian Journal of Medical Ethics, http://ijme.in/articles/the-chennai-floods-of-2015-and-the-health-system-response/?galley=html (accessed 3 October 2023).

[ref18] 18. Dasgupta A, Bandyopadhyay S, Bandyopadhyay L, et al. How frail are our elderly? An assessment with Tilburg frailty indicator (TFI) in a rural elderly population of West Bengal. J Family Med Prim Care 2019; 8: 2242–2248. pmid:31463237
View Article
PubMed/NCBI
Google Scholar

[54] View Article

[55] PubMed/NCBI

[56] Google Scholar

[ref19] 19. Bell SA, Choi H, Langa KM, et al. Health risk behaviors after disaster exposure among older adults. Prehospital and disaster medicine 2019; 34: 95–97. pmid:30642407
View Article
PubMed/NCBI
Google Scholar

[58] View Article

[59] PubMed/NCBI

[60] Google Scholar

[ref20] 20. Cesari M, Proietti M. COVID-19 in Italy: Ageism and Decision Making in a Pandemic. J Am Med Dir Assoc 2020; 21: 576–577. pmid:32334771
View Article
PubMed/NCBI
Google Scholar

[62] View Article

[63] PubMed/NCBI

[64] Google Scholar

[ref21] 21. Cohn-Schwartz E, Ayalon L. Societal Views of Older Adults as Vulnerable and a Burden to Society During the COVID-19 Outbreak: Results From an Israeli Nationally Representative Sample. J Gerontol B Psychol Sci Soc Sci 2021; 76: e313–e317. pmid:32882025
View Article
PubMed/NCBI
Google Scholar

[66] View Article

[67] PubMed/NCBI

[68] Google Scholar

[ref22] 22. Kessler E-M, Bowen CE. COVID ageism as a public mental health concern. The Lancet Healthy Longevity 2020; 1: e12. pmid:34173613
View Article
PubMed/NCBI
Google Scholar

[70] View Article

[71] PubMed/NCBI

[72] Google Scholar

[ref23] 23. Lichtenstein B. From ‘Coffin Dodger’ to ‘Boomer Remover’: Outbreaks of Ageism in Three Countries With Divergent Approaches to Coronavirus Control. J Gerontol B Psychol Sci Soc Sci 2021; 76: e206–e212. pmid:32719851
View Article
PubMed/NCBI
Google Scholar

[74] View Article

[75] PubMed/NCBI

[76] Google Scholar

[ref24] 24. Meisner BA. Are you OK, Boomer? Intensification of ageism and intergenerational tensions on social media amid COVID-19. Leisure Sciences 2020; 43: 56–61.
View Article
Google Scholar

[78] View Article

[79] Google Scholar

[ref25] 25. Previtali F, Allen LD, Varlamova M. Not Only Virus Spread: The Diffusion of Ageism during the Outbreak of COVID-19. J Aging Soc Policy 2020; 32: 506–514. pmid:32507060
View Article
PubMed/NCBI
Google Scholar

[81] View Article

[82] PubMed/NCBI

[83] Google Scholar

[ref26] 26. Cabin S, Jardin E. Why Preventing Ageist Attitudes Is Not Enough during COVID-19 Pandemic. Health Soc Work 2021; hlab008. pmid:33956967
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref27] 27. Kang H, Kim H. Ageism and Psychological Well-Being Among Older Adults: A Systematic Review. Gerontol Geriatr Med 2022; 8: 23337214221087023. pmid:35434202
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref28] 28. Rothermund K, Klusmann V, Zacher H. Age Discrimination in the Context of Motivation and Healthy Aging. J Gerontol B Psychol Sci Soc Sci 2021; 76: S167–S180. pmid:34515776
View Article
PubMed/NCBI
Google Scholar

[93] View Article

[94] PubMed/NCBI

[95] Google Scholar

[ref29] 29. Lai DWL, Liu EHS, Yan ECW, et al. Exposure to socio-political unrest and wellbeing of older people in Hong Kong. BMC Geriatr 2022; 22: 768. pmid:36151516
View Article
PubMed/NCBI
Google Scholar

[97] View Article

[98] PubMed/NCBI

[99] Google Scholar

[ref30] 30. Charlson F, van Ommeren M, Flaxman A, et al. New WHO prevalence estimates of mental disorders in conflict settings: a systematic review and meta-analysis. Lancet 2019; 394: 240–248. pmid:31200992
View Article
PubMed/NCBI
Google Scholar

[101] View Article

[102] PubMed/NCBI

[103] Google Scholar

[ref31] 31. Bodas M, Peleg K, Stolero N, et al. Risk perception of natural and human-made disasters—cross sectional study in eight countries in Europe and beyond. Frontiers in public health 2022; 10: 825985. pmid:35252099
View Article
PubMed/NCBI
Google Scholar

[105] View Article

[106] PubMed/NCBI

[107] Google Scholar

[ref32] 32. De Rubeis V, Lee J, Anwer MS, et al. Impact of disasters, including pandemics, on cardiometabolic outcomes across the life-course: a systematic review. BMJ open 2021; 11: e047152. pmid:33941635
View Article
PubMed/NCBI
Google Scholar

[109] View Article

[110] PubMed/NCBI

[111] Google Scholar

[ref33] 33. National Disaster Management Authority. National Disaster Management Plan: A comprehensive step towards reducing disaster risk ‐ India. Ministry of Home Affairs, GoI, https://reliefweb.int/report/india/national-disaster-management-plan-comprehensive-step-towards-reducing-disaster-risk (04 2021, accessed 31 March 2024).

[ref34] 34. International Institute for Population Sciences (IIPS) NP for, Health Care of Elderly (NPHCE), MoHFW HTHCS of, (USC) PH (HSPH) and the U of SC. Longitudinal Ageing Study in India (LASI) Wave 1, 2017–18, India Report. 2020.

[ref35] 35. Idler EL, Benyamini Y. Self-rated health and mortality: a review of twenty-seven community studies. Journal of health and social behavior 1997; 21–37. pmid:9097506
View Article
PubMed/NCBI
Google Scholar

[115] View Article

[116] PubMed/NCBI

[117] Google Scholar

[ref36] 36. Muhammad T, Meher T, Sekher TV. Association of elder abuse, crime victimhood and perceived neighbourhood safety with major depression among older adults in India: a cross-sectional study using data from the LASI baseline survey (2017–2018). BMJ open 2021; 11: e055625. pmid:34907072
View Article
PubMed/NCBI
Google Scholar

[119] View Article

[120] PubMed/NCBI

[121] Google Scholar

[ref37] 37. StataCorp LP. Stata 15 Base Reference Manual. College Station, TX, StataCorp LLC, https://www.merit.unu.edu/wp-content/docs/stata/%5BM%5DMATAReferenceManual_Release13.pdf (2017, accessed 25 March 2024).

[ref38] 38. Agyapong VI, Ritchie A, Brown MR, et al. Long-term mental health effects of a devastating wildfire are amplified by socio-demographic and clinical antecedents in elementary and high school staff. Frontiers in psychiatry 2020; 11: 448. pmid:32528323
View Article
PubMed/NCBI
Google Scholar

[124] View Article

[125] PubMed/NCBI

[126] Google Scholar

[ref39] 39. Agyapong VI, Juhas M, Omege J, et al. Prevalence rates and correlates of likely post-traumatic stress disorder in residents of fort mcmurray 6 months after a wildfire. International Journal of Mental Health and Addiction 2021; 19: 632–650.
View Article
Google Scholar

[128] View Article

[129] Google Scholar

[ref40] 40. Wilson-Genderson M, Heid AR, Pruchno R. Onset of multiple chronic conditions and depressive symptoms: a life events perspective. Innovation in aging 2017; 1: igx022. pmid:30480117
View Article
PubMed/NCBI
Google Scholar

[131] View Article

[132] PubMed/NCBI

[133] Google Scholar

[ref41] 41. Tortella-Feliu M, Fullana MA, Pérez-Vigil A, et al. Risk factors for posttraumatic stress disorder: An umbrella review of systematic reviews and meta-analyses. Neuroscience & Biobehavioral Reviews 2019; 107: 154–165.
View Article
Google Scholar

[135] View Article

[136] Google Scholar

[ref42] 42. Wu H-C, Chou P, Huang-Chih Chou F, et al. Survey of quality of life and related risk factors for a Taiwanese village population 3 years post-earthquake. Australian & New Zealand Journal of Psychiatry 2006; 40: 355–361.
View Article
Google Scholar

[138] View Article

[139] Google Scholar

[ref43] 43. Kılıc C, Aydın İ, Taşkıntuna N, et al. Predictors of psychological distress in survivors of the 1999 earthquakes in Turkey: effects of relocation after the disaster. Acta Psychiatr Scand 2006; 114: 194–202. pmid:16889590
View Article
PubMed/NCBI
Google Scholar

[141] View Article

[142] PubMed/NCBI

[143] Google Scholar

[ref44] 44. Bandla S, Nappinnai NR, Gopalasamy S. Psychiatric morbidity in December 2015 flood-affected population in Tamil Nadu, India. International journal of social psychiatry 2019; 65: 338–344. pmid:31068043
View Article
PubMed/NCBI
Google Scholar

[145] View Article

[146] PubMed/NCBI

[147] Google Scholar

[ref45] 45. Thomas CR. Psychiatric Sequelae of Disasters. Journal of Burn Care & Research 2006; 27: 600–605. pmid:16998391
View Article
PubMed/NCBI
Google Scholar

[149] View Article

[150] PubMed/NCBI

[151] Google Scholar

[ref46] 46. Gao X, Leng Y, Guo Y, et al. Association between earthquake experience and depression 37 years after the Tangshan earthquake: a cross-sectional study. BMJ open 2019; 9: e026110. pmid:31434762
View Article
PubMed/NCBI
Google Scholar

[153] View Article

[154] PubMed/NCBI

[155] Google Scholar

[ref47] 47. Arnberg FK, Eriksson N-G, Hultman CM, et al. Traumatic bereavement, acute dissociation, and posttraumatic stress: 14 years after the MS Estonia disaster. Journal of traumatic stress 2011; 24: 183–190. pmid:21442665
View Article
PubMed/NCBI
Google Scholar

[157] View Article

[158] PubMed/NCBI

[159] Google Scholar

[ref48] 48. Yang YK, Yeh TL, Chen CC, et al. Psychiatric morbidity and posttraumatic symptoms among earthquake victims in primary care clinics. General Hospital Psychiatry 2003; 25: 253–261. pmid:12850657
View Article
PubMed/NCBI
Google Scholar

[161] View Article

[162] PubMed/NCBI

[163] Google Scholar

[ref49] 49. Ticehurst S, Webster RA, Carr VJ, et al. The Psychosocial Impact of an Earthquake on the Elderly. International Journal of Geriatric Psychiatry 1996; 11: 943–951.
View Article
Google Scholar

[165] View Article

[166] Google Scholar

[ref50] 50. Khan M, Ilcisin M, Saxton K. Multifactorial discrimination as a fundamental cause of mental health inequities. Int J Equity Health 2017; 16: 43. pmid:28257630
View Article
PubMed/NCBI
Google Scholar

[168] View Article

[169] PubMed/NCBI

[170] Google Scholar

[ref51] 51. Dyer CB, Regev M, Burnett J, et al. SWiFT: a rapid triage tool for vulnerable older adults in disaster situations. Disaster medicine and public health preparedness 2008; 2: S45–S50. pmid:18769267
View Article
PubMed/NCBI
Google Scholar

[172] View Article

[173] PubMed/NCBI

[174] Google Scholar

[ref52] 52. Kuroda Y, Iwasa H, Orui M, et al. Risk factor for incident functional disability and the effect of a preventive exercise program: a 4-year prospective cohort study of older survivors from the great East Japan earthquake and nuclear disaster. International journal of environmental research and public health 2018; 15: 1430. pmid:29986471
View Article
PubMed/NCBI
Google Scholar

[176] View Article

[177] PubMed/NCBI

[178] Google Scholar

[ref53] 53. Ardalan A, Mazaheri M, Vanrooyen M, et al. Post-disaster quality of life among older survivors five years after the Bam earthquake: implications for recovery policy. Ageing & Society 2011; 31: 179–196.
View Article
Google Scholar

[180] View Article

[181] Google Scholar

[ref54] 54. Zhang J-L, He H-C, Lin H-D, et al. Motor function and activities of daily living capacity of patients with fractures sustained during the Wenchuan earthquake. Chinese medical journal 2011; 124: 1504–7. pmid:21740806
View Article
PubMed/NCBI
Google Scholar

[183] View Article

[184] PubMed/NCBI

[185] Google Scholar

[ref55] 55. Courtin E, Knapp M. Social isolation, loneliness and health in old age: a scoping review. Health & social care in the community 2017; 25: 799–812. pmid:26712585
View Article
PubMed/NCBI
Google Scholar

[187] View Article

[188] PubMed/NCBI

[189] Google Scholar

[ref56] 56. Conrad KA, Amstadter AB, McCauley JL, et al. Examination of general health following typhoon Xangsane: A pre–postanalysis. International Perspectives in Psychology: Research, Practice, Consultation 2011; 1: 28.
View Article
Google Scholar

[191] View Article

[192] Google Scholar

[ref57] 57. Sannabe A, Aida J, Wada Y, et al. On the direct and indirect effects of the Great East Japan earthquake on self rated health through social connections: Mediation analysis. Japan and the World Economy 2020; 56: 101039. pmid:35814635
View Article
PubMed/NCBI
Google Scholar

[194] View Article

[195] PubMed/NCBI

[196] Google Scholar

[ref58] 58. Hu Y-N, Hu G-C, Hsu C-Y, et al. Assessment of individual activities of daily living and its association with self-rated health in elderly people of Taiwan. International Journal of Gerontology 2012; 6: 117–121.
View Article
Google Scholar

[198] View Article

[199] Google Scholar

[ref59] 59. Demirchyan A, Petrosyan V, Armenian HK, et al. Prospective study of predictors of poor self-rated health in a 23-year cohort of earthquake survivors in Armenia. Journal of epidemiology and global health 2015; 5: 265–274. pmid:26231402
View Article
PubMed/NCBI
Google Scholar

[201] View Article

[202] PubMed/NCBI

[203] Google Scholar

[ref60] 60. Caetano SC, Silva CM, Vettore MV. Gender differences in the association of perceived social support and social network with self-rated health status among older adults: A population-based study in Brazil. BMC Geriatrics; 13. Epub ahead of print 2013. pmid:24229389
View Article
PubMed/NCBI
Google Scholar

[205] View Article

[206] PubMed/NCBI

[207] Google Scholar

[ref61] 61. Lemes JS, Pagotto V, Rodrigues PKA, et al. Association between health self-assessment and types of activities of daily living in older adults. Cadernos Saúde Coletiva 2021; 29: 251–259.
View Article
Google Scholar

[209] View Article

[210] Google Scholar

[ref62] 62. Miller AC, Arquilla B. Chronic diseases and natural hazards: impact of disasters on diabetic, renal, and cardiac patients. Prehospital and disaster medicine 2008; 23: 185–194. pmid:18557300
View Article
PubMed/NCBI
Google Scholar

[212] View Article

[213] PubMed/NCBI

[214] Google Scholar

[ref63] 63. Gohardehi F, Seyedin H, Moslehi S. Prevalence rate of diabetes and hypertension in disaster-exposed populations: a systematic review and meta-analysis. Ethiopian Journal of Health Sciences; 30. pmid:32874087
View Article
PubMed/NCBI
Google Scholar

[216] View Article

[217] PubMed/NCBI

[218] Google Scholar

[ref64] 64. Brackbill RM, Graber JM, Robison WA (Allen). Editorial for “Long-Term Health Effects of the 9/11 Disaster” in International Journal of Environmental Research and Public Health, 2019. Int J Environ Res Public Health 2019; 16: 3289.
View Article
Google Scholar

[220] View Article

[221] Google Scholar

[ref65] 65. Ligon BL. Infectious diseases that pose specific challenges after natural disasters: a review. In: Seminars in pediatric infectious diseases. Elsevier, 2006, pp. 36–45. pmid:16522504
View Article
PubMed/NCBI
Google Scholar

[223] View Article

[224] PubMed/NCBI

[225] Google Scholar

[ref66] 66. Vollaard AM, Ali S, van Asten HA, et al. Risk factors for typhoid and paratyphoid fever in Jakarta, Indonesia. Jama 2004; 291: 2607–2615. pmid:15173152
View Article
PubMed/NCBI
Google Scholar

[227] View Article

[228] PubMed/NCBI

[229] Google Scholar

[ref67] 67. Watson JT, Gayer M, Connolly MA. Epidemics after natural disasters. Emerging infectious diseases 2007; 13: 1. pmid:17370508
View Article
PubMed/NCBI
Google Scholar

[231] View Article

[232] PubMed/NCBI

[233] Google Scholar

[ref68] 68. Phraknoi N, Sutanto J, Hu Y, et al. Older people’s needs in urban disaster response: A systematic literature review. International Journal of Disaster Risk Reduction 2023; 96: 103809.
View Article
Google Scholar

[235] View Article

[236] Google Scholar

Figures

Abstract

Background

Methods

Results

Conclusions

Background

Conceptual framework

Consequences of disasters for older adults

Methods

Study design and participants

Outcome variables.

Key explanatory variable.

Others explanatory variables.

Statistical analysis

Multivariate multilevel analysis fit statistics.

Results

Socio-demographic profile and state-wise prevalence of natural and/or human-made disaster in the study sample

Natural and/or human-made disaster and associated health outcomes among older adults

Discussion

Policy implications

Conclusions

Strengths and limitations

Supporting information

S1 Table. Model diagnostics of the multivariate multilevel logistic regression analyses of health outcomes by natural and/or human-made disasters among older adults.

S2 Table. Multivariate multilevel logistic regression analyses of health outcomes by natural or human-made disasters and by background characteristics of older adults.

References