Environmental Displacement and Mental Well-Being in Banjarnegara, Indonesia

Abstract

Background:

Residential moves (displacement) owing to climate- and weather-related disasters may significantly impact mental health. Despite the growing risk from climate change, health impacts of environmental-mobility remain understudied.

Objectives:

We assessed the effects of displacement on the association between landslides and changes in perceived mental well-being in Banjarnegara, Indonesia. We also investigated whether sociodemographics (age, sex, level of education, household-level income, or employment in agriculture) and landslide characteristics (number and severity of landslides) were associated with differing odds of relocation after experiencing landslides.

Methods:

In this cross-sectional study, we surveyed 420 individuals who experienced landslides between 2014 and 2018 to assess perceived changes in mental well-being, comparing after landslide exposure to before landslide exposure. We used a novel six-item measure that was created in collaboration with the local community to compare perceived changes between those who were displaced by landslides and those who were not displaced, using logistic and multinomial regressions adjusted for sociodemographic characteristics. We then assessed whether the odds of displacement differed based on sociodemographic characteristics and landslide exposure characteristics, using logistic regressions.

Results:

Those who were displaced were more likely than those who were not displaced to report perceived increases in economic stability [$\text{odds ratio\hspace{0.17em}}\left(\text{OR}\right)=3.06$; 95% confidence interval (CI): 1.45, 6.46], optimism ($\text{OR}=4.01$; 95% CI: 1.87, 8.61), safety ($\text{OR}=2.71$; 95% CI: 1.44, 5.10), religiosity ($\text{OR}=1.92$; 95% CI: 1.03, 3.65), and closeness with community ($\text{OR}=1.90$; 95% CI: 1.10, 3.33) after landslides compared with before their first landslide during the study period. More frequent landslide exposures were associated with reduced odds of relocation, but more severe landslides were associated with increased odds of relocation.

Discussion:

These findings suggest that landslides affect the mental well-being not only of those who are displaced but also of those who are left behind. Further, this work supports the need for community-based participatory research to fully capture the health impacts of environmental mobility. https://doi.org/10.1289/EHP9391

Introduction

Environmental events have long been associated with population movement, through both direct (e.g., floods, hurricanes, wildfires) (Black et al. 2011a) and indirect exposures (e.g., disruption of food systems because of drought) (McMichael et al. 2012). This mobility may result in adverse health outcomes via a number of complex pathways, which vary depending on the context and type of movement (McMichael et al. 2012). Environmentally driven displacement usually occurs over short distances (Roland and Curtis 2020) and within country borders (Findlay 2011) and may be temporary or permanent (Black et al. 2011b). The term displacement is often used in contrast to the term migration, the latter of which implies a greater degree of agency and choice on the part of the migrant and may encompass planned relocations prior to disasters (Black et al. 2011a). These distinctions have been widely discussed elsewhere (Heslin et al. 2019). In this paper, we focus on displacement following landslides in Indonesia (hereafter referred to as landslide-displacement).

In 2016, nearly $1\text{\hspace{0.17em}million}$ people were internally displaced in Indonesia because of rainy seasons floods and landslides (UNDP 2018). Landslides are among the most frequent disasters in Indonesia and result from stress in the soil layer of a slope during periods of heavy rainfall, which can lead to an overall reduction in slope stability (Crozier 2010). More than 50% of the Indonesian terrain is vulnerable to landslides (Karnawati et al. 2013, 2018), a risk that has been exacerbated by deforestation and land clearing (Crozier 2010; Runyan and D’Odorico 2014). Extant research on landslides has focused primarily on the link between climate change and landslides (Crozier 2010), as well as hazard mapping (Hadmoko et al. 2010; Wati et al. 2010), estimating economic losses (Schuster and Fleming 1986), the creation of early warning systems (Liao et al. 2010), and other risk reduction strategies (Duncan and Brebbia 2009). However, the relationship between landslides and displacement has not yet been explored fully, and we know even less about the effects of landslide-displacement on health.

Our project helps to fill this gap by focusing on the mental health impacts of landslides in Banjarnegara, Indonesia. Our primary objective was to compare the mental well-being of those who were displaced by landslides to those who experienced landslides but were not displaced. Banjarnegara is a regency in Central Java and is highly vulnerable to landslides (Setiawan et al. 2019). A limited number of studies have explored psychological impacts of landslides, finding that landslides are associated with adverse mental health outcomes (even more so than other climate- and weather-related disasters) (Kennedy et al. 2015). However, to the best of our knowledge, research has not examined the relationship between displacement and mental health in the context of landslides. Landslide-displacement may disrupt social networks, separate families and friends, and fragment communities (Torres and Casey 2017), all of which are important sources of stability and coping after disasters (McMichael et al. 2012). Further, displacement may hinder access to health care and infrastructure, which can contribute to negative mental health outcomes (Fussell and Lowe 2014). Previous research in other locations has shown that displacement after climate- and weather-related disasters can increase the odds of depression, anxiety, and post-traumatic stress disorder (PTSD) (Munro et al. 2017). Thus, we hypothesized that those who are displaced by landslides have increased risk of adverse mental health outcomes compared with those who experienced landslides but were not displaced.

To assess mental health, we used a novel six-item measure created in collaboration with the local community. This community-informed approach allows a more holistic understanding of mental health than reliance solely on standard measures of mental health (e.g., depression or anxiety), which are often developed by and intended for Western populations (Osborn et al. 2021). Throughout this paper, we primarily rely on the term mental well-being in lieu of mental health to encompass this broader conceptualization.

This study contributes to the existing literature on environmental displacement and health in Indonesia. Although a number of studies have considered the role of the environment in influencing population mobility in Indonesia, these studies have not considered potential health impacts of displacement and migration, nor landslides in particular (Bohra-Mishra et al. 2014; Thiede and Gray 2017). Further, we focus on a low-income population: More than half of our study population earned less than the minimum wage, which at $\sim \$3.30\text{\hspace{0.17em}USD}/\mathrm{d}$, as designated by the Banjarnegara Regency, is the lowest minimum wage in Indonesia (Nugroho 2017). For comparison, since 2017 the federal minimum wage in the United States has been $\$7.25\text{\hspace{0.17em}USD}/\mathrm{h}$ (U.S. Department of Labor 2020). These low-income populations may face the most damaging effects of disasters and global environmental change but have the least capacity to respond. This work is important given that landslides are expected to become more frequent and widespread under conditions of climate change (Ahmad et al. 2019). A robust body of literature (Black et al. 2011a; Obokata et al. 2014) has explored displacement and migration related to climate change, and more recently, scholars have begun to consider how this population movement may impact human health (Mazhin et al. 2020; Schwerdtle et al. 2018). In order to address the potential public health implications of mobility due to climate change, we must first more fully understand the effects of environmental mobility on health today.

Methods

Study Design

This cross-sectional study was conducted in the regency of Banjarnegara in Indonesia. Banjarnegara is located in the province of Central Java and has a population of almost 870,000 (based on the 2010 census) (BPS 2018). In 2019, Banjarnegara had an average population density of 863 people/km² (BPS 2019). The regency frequently experiences flooding and landslides, and the population is particularly vulnerable to displacement because settlements and farmland are concentrated on steep slopes.

The primary aim of this project was to assess the effect of displacement on the association between landslides and mental health. We used a locally relevant survey measure of well-being, developed in conjunction with the community. In this study, we applied that measure of well-being in relation to exposure to landslides and landslide-displacement. First, we conducted a pilot study, and then the full survey.

Sampling Strategy

Data for both the pilot and the full survey were collected via questionnaires distributed door-to-door to households in landslide-affected communities from June to September 2019. Our local partners discouraged data collection during the rainy season (November–March) to reduce the risk to our surveying team. Data were collected by the first author and a team of research assistants (fluent in English, Bahasa Indonesia, and Javanese) who were recruited by partners at the University of Indonesia.

Because we did not have a complete sampling frame or population registry for our target population, we used snowball sampling to locate participants. Snowball sampling is a type of convenience-based sampling in which participants are asked to identify other potential subjects (Shaghaghi et al. 2011). Nonprobability-based sampling approaches, including snowball sampling, are frequently employed when studying migrant populations because of the difficulties in locating transient populations (Fussell et al. 2014). Our sampling strategy was developed with our local partners as a culturally appropriate and effective way to access this highly mobile population; our local partners expressed concerns that local census or mapping approaches might not yield a sufficient sample size for this transient, low-income population. Snowball sampling allowed us to locate both participants who experienced landslides but did not relocate and participants who experienced landslides and relocated to other areas in Banjarnegara. This approach has an advantage over sampling only within landslide-affected areas, which would have excluded participants who relocated to other areas (Fussell et al. 2014). However, our catchment area did not extend beyond Banjarnegara, which means that we did not include those who moved over long distances (e.g., relocated to Jakarta).

We began our snowball sampling with local leaders who acted as “key informants.” These key informants were identified by our local partners and included desa (village) leaders, school teachers, and small business owners, all of whom were active members of the community. We intentionally began our surveying with a mix of key informants who had been displaced by landslides and those who had not been displaced by landslides. The use of multiple key informants was intended to expand the diversity of our sample and assist in accessing this hard-to-reach and understudied population (Kirchherr and Charles 2018).

Surveys were administered orally in Bahasa Indonesia and responses were recorded on a computer tablet. Participants were included if they had experienced at least one landslide in Banjarnegara between 2014 and 2018 and if they were $\ge 18$ years of age at the time of their first landslide exposure (because minors may not have agency in the migration decision-making process). Participants in both the pilot study and the full survey were gifted small tokens (value of $\sim \$5\text{\hspace{0.17em}USD}$), consisting of a Yale University pen and a small package of toiletry items (deodorant, toothpaste, toothbrush, etc.) to compensate them for their time. Gifts were selected at the recommendation of local partners and distributed after participants finished the survey (in part or in whole). After the survey, participants were asked to provide references for other potential study participants who had experienced landslides during the study period and who were residing in Banjarnegara (either those who were displaced within the regency or those who did not relocate). Referrals came in multiple forms: specific names, households, or general areas in which people who were displaced by landslides were thought to have moved. Interviewers followed up on every name, household, and location that was provided.

Ethical approval for this study was granted by the Yale University institutional review board and Committee on Research Ethics at the Faculty of Psychology at University of Indonesia. All respondents provided informed consent.

Survey Measures

The survey was piloted prior to full implementation. The pilot consisted of 30 villagers and 5 village leaders. We assessed how respondents understood and responded to the survey using cognitive interviewing, a common method for evaluating questionnaires and identifying areas of possible response error (Willis 2004). After the pilot survey, we made minor revisions to the questionnaire to improve unclear questions before the full survey administration. In the full survey, we collected questionnaire data from 420 respondents living in Banjarnegara, Indonesia, who had experienced one or more landslides from 2014 to 2018.

Our survey included sociodemographic data on sex, date of birth, level of education, annual household income, employment status, religion, and whether a person was employed in agriculture at the time of the interview (e.g., age at the time of the interview). We also collected data on exposure to landslides from 2014 to 2018. Participants were provided with the definition of a landslide (“the sliding or falling of a mass of rocks, earth, or soil down a slope”) and were asked to recall the number of landslides they experienced during the study period and the date of each landslide. We generated a variable to represent the severity of each respondent’s exposure to landslides (hereafter termed personal landslide exposure). To assess personal landslide exposure, we asked respondents to indicate whether they had experienced any of following harms as a result of each landslide: emotional harm, physical harm, physical harm to a friend or family member, or death of a friend or family member. For each landslide, respondents assessed housing damage and workplace damage, separately, as no damage, minimal damage, significant damage, or major damage or destruction. Based on the results, we created a composite score for each respondent and landslide by summing positive responses based on the number of harms they reported (0–4); the level of damage to their home (0–3); and the level of damage to their workplace (0–3). The overall scale, based on the sum of scores for harms, damage to the home, and damage to the workplace, ranged from 0–10 with each point increase on the scale representing exposure to a harm (e.g., emotional harm) or more severe level of damage to home or workplace (e.g., major damage vs. minor damage). This approach was modeled after similar scales used in hurricane research (Lowe and Rhodes 2013).

Mental well-being was assessed using a community-based measure consisting of six survey items. This measure was developed with the community in conjunction with an earlier qualitative study in the same community (Burrows et al. 2021), in which community members were asked to describe their own understandings of mental health (e.g., “What does mental health mean to you?”). This approach allowed the community to participate in the survey design and to define their own concepts of mental health. The resulting measure consisted of six components of well-being: feelings of religiousness, economic stability, optimism about the future, sense of safety and security, closeness with family, and closeness with community. For the questionnaire in this study, each of these six items was assessed using a five-point Likert-type (Arnold et al. 1967) scale to assess perceived changes, comparing before and after landslide exposures. We asked participants, “Compared with before your first landslide (in the 2014–2018 study period), have you experienced changes in any of the six dimensions of mental well-being?” For example, for economic stability, we asked participants to compare the current day to before their first landslide during the study period to assess whether they perceived a) much less economic stability, b) somewhat less economic stability, c) no change in economic stability, d) somewhat more economic stability, or e) much more economic stability. This allowed each study participant to give their own self-perception of how landslides during the study period impacted their mental well-being. The measure also allowed us to investigate impacts of multiple exposures and displacements: Each respondent replied to the set of mental well-being questions only once, and we recorded the number of landslides they experienced during the study period.

We asked respondents whether they had had to leave their home (for $>2\mathrm{wk}$) because of a landslide between 2014 and 2018. We specified $>2\mathrm{wk}$ in order to exclude those who evacuated away from the immediate threat of the landslide but returned home within hours or days. Instead, we focused on those who were unable to return home for an extended period of time (weeks or months of temporary displacement) and those who relocated permanently. This means that we included participants who were both temporarily displaced and those who permanently relocated, as long as they were still residing within Banjarnegara. For those who reported displacement, we also collected information on the characteristics of their moves. These included the number of times a person was displaced over the 5-y period (2014–2018) and, for each move, the amount of time spent in unstable (i.e., temporary) housing, the distance of displacement, whether the move was made with other members of the community or independently (as a family or as an individual), and whether the respondent was still unstably or temporarily housed at the time of the interview or not (this allowed us to differentiate between temporary and permanent relocations).

Descriptive statistics were calculated for sociodemographic, landslide, and displacement characteristics, as well as for mental well-being. Descriptive statistics were analyzed using chi-squared tests and $t$-tests, as appropriate.

Statistical Analyses

Exploratory factor analysis.

We began by evaluating the community-based measure for mental well-being. We tested for correlation in perceived changes in each of the six variables (religiosity, closeness with family, closeness with community, economic stability, optimism, and sense of safety) using Kendall’s rank correlation coefficient. We then used an exploratory factor analysis, a common approach to help evaluate survey instruments, to identify a factor structure present in a set of variables (exploratory factor analysis differs from confirmatory factor analysis, which tests a number of hypothetical models to see which best fit the data) (Swisher et al. 2004). We used a varimax rotation with Kaiser normalization, retained factors with $\text{Eigenvalues}>1$, and used a factor loading of $>0.40$.

The association between landslides and mental well-being.

Our primary analyses compared whether perceived changes in well-being (comparing before and after exposure to landslides during the 5-y study period) differed for those who experienced landslides and were displaced ($n=180$) and those who experienced landslides but were not displaced ($n=240$). We used multinomial and logistic regressions and treated displacement as the exposure of interest. This modeling procedure followed the approach used by other studies to assess the effects of displacement after weather- and climate-related disasters on health (e.g., Munro et al. 2017). We conducted two sensitivity analyses. In the first, we recalculated standard errors using Huber-White sandwich estimators to account for possible clustering [following the precedent set by other research with similar research aims (Munro et al. 2017)]. In the second, we assessed the impact of landslides-displacement on each of the factors derived by the exploratory factor analysis using generalized linear regression models.

We collapsed the five-point Likert-type scale into three response options in order to preserve statistical power, which allowed us to compare whether a person perceived no change before and after landslides, change in one direction, or change in a different direction. For example, for religion, we collapsed the categories to assess whether a person perceived that they had: a) become less religious, b) experienced no change, or c) became more religious after landslides (comparing before and after exposure to landslides during the 5-y study period).

The relationship between landslides, displacement, and health remains complex. Therefore, we also considered whether displacement modified the association between sociodemographic characteristics (age, sex, level of education, household-level income, or employment in agriculture) and perceived change in mental well-being after landslides differed among those who were displaced and those who were not displaced. We first assessed whether perceived change after landslides for each of the six characteristics of mental well-being differed based on sociodemographic characteristics. We next introduced interaction terms between displacement and sociodemographics. Significant interactions were investigated further by stratifying each of the six models (one per characteristic of mental well-being) by displacement (moved or did not move after landslide exposure).

Mobility patterns.

As a secondary analysis, we investigated who was most likely to relocate given exposure to landslides, using a logistic regression to assess whether the odds of displacement differed based on sociodemographic characteristics (age, sex, level of education, household income, or employment in agriculture).

Finally, we restricted the data set to those who were displaced ($n=240$) in order to consider whether move-based characteristics (time spent in temporary housing, distance of displacement, whether a person was still displaced at the time of the interview, whether they received assistance to relocate or not, and number of displacements) were associated with a perceived change in well-being (comparing before and after exposure to landslides during the 5-y study period).

All models were adjusted for sociodemographic characteristics at the time of the interview [age ($<31$, 31–43, 44–54, $\ge 55$ y), level of education (no education, primary school, more than primary school), sex (men, women), household income (above minimum wage, below minimum wage), employed in agriculture (yes, no)] and landslide exposure characteristics [number of landslides (count), personal landslide exposure (scale from 0–10), time since last exposure (in years)]. We tested for correlations between the sociodemographic characteristics and between the landslide characteristics using Spearman’s rank correlations. Participants with missing sociodemographic data were excluded from the adjusted analyses ($n=15$). For all analyses, results were considered statistically significant if $p$-values were $<0.05$. All statistical analyses were performed in R Studio (version 1.1.463; R Studio Team).

Results

Summary Statistics

Our snowball sampling process resulted in 420 participants, 240 of whom reported being displaced by landslides and 180 of whom reported experiencing landslides but were not displaced. Our response rate was 95.24%, with only 21 eligible participants declining to participate.

Table 1 lists the descriptive statistics for the sociodemographics of those who were displaced compared with those who were not displaced by landslides. Although participants with missing data were excluded from the adjusted analyses, in Table 1 we include the number of participants with each value, including missing values, to provide a fuller understanding of the data collected by this study. The majority of respondents in both groups were female (70.0% not displaced, 61.2% displaced, $p=0.063$). Age categories did not differ significantly between the two groups. The level of education at the time of the interview was low among respondents in both groups: Only 25.0% of those who were not displaced and 30.4% of those who were displaced had received more than primary school education ($p=0.387$). The majority of both groups reported earning less than the minimum wage at the time of the interview; however, a higher percentage of those who were not displaced reported earning less than the minimum wage compared with those who were displaced (68.3% vs. 52.1%, respectively, $p<0.001$). Approximately half of all participants were employed in agriculture at the time of the interview (48.6% of those not displaced and 53.3% of those who were displaced, $p=0.338$).

Table 1.

Sociodemographic statistics for those who were displaced and those who were not displaced [$n$ (%)] by landslides in Banjarnegara, Indonesia, between 2014 and 2018 ($n=420$).

Variables	Not displaced ($n=180$)	Displaced ($n=240$)	$p$-Value
Sex			0.063
Male	54 (30.0)	93 (38.8)
Female	126 (70.0)	147 (61.2)
Missing	0	0
Age (y)			0.207
$<\mathrm{\hspace{0.17em}31}$	39 (22.2)	59 (25.7)
31–43	39 (22.2)	66 (28.7)
44–54	51 (29.0)	50 (21.7)
$\ge 55$	47 (26.7)	55 (23.9)
Missing	4	10
Level of education			0.387
No education	40 (22.2)	44 (18.3)
Primary schoola	95 (52.8)	123 (51.2)
More than primary school	45 (25.0)	73 (30.4)
Missing	0	0
Level of incomeb			$<0.001$
Less than the minimum wage	57 (31.7)	115 (47.9)
More than the minimum wage	123 (68.3)	125 (52.1)
Missing	0	0
Employment			0.338
Not employed in agriculture	92 (51.4)	112 (46.7)
Employed in agriculture	87 (48.6)	128 (53.3)
Missing	1	0

Note: Percentages may not sum to 100% owing to rounding. $p$-Values are for ${\mathrm{\chi }}^2$ tests. Values for age, education, income, and employment are based on information at the time of the interview.

^aIn Indonesia, the last year of primary school is comparable to the sixth grade in the United States (children are usually 11–12 years of age).

^bThe minimum wage was classified by the Central Java Provincial Government for the Banjarnegara Regency in 2018 as $<\mathrm{1,500,000}$ Indonesia rupiah per month. This is equivalent to around $\$3.33\text{\hspace{0.17em}USD}/\mathrm{d}$.

Table 2 presents the characteristics of exposure to landslides (e.g., number of landslides) for those who were displaced by landslides and those who were not displaced. All landslide characteristics were significantly different between the two groups. Those who were displaced experienced fewer landslides than those who were not displaced [average of 1.09 vs. 1.18 landslides over the 5-y study period (2014–2018), $p=0.023$], and they experienced fewer landslides per year after their first exposure to landslides (an average of 0.55 vs. 0.78 landslides per year, $p<0.001$). However, those who were displaced experienced more severe landslides (average personal landslide exposure of 4.50 per landslide vs. 1.78, $p<0.001$). At the time of the interview, those who were displaced had an average time since last exposure (i.e., last landslide) of 3.01 y, whereas for those who were not displaced, the average time since last exposure was 1.97 y ($p<0.001$). Results on the personal landslide exposure scale ranged from 0–10, with an interquartile range of 3.08.

Table 2.

Characteristics of landslide exposure comparing those who were displaced and those who were not displaced [mean (SD)] in Banjarnegara, Indonesia, between 2014 and 2018 ($n=420$).

Exposure characteristic	Description	Displaced ($n=\mathrm{240}$)	Not displaced ($n=18\mathrm{0}$)	$p$-Value
Number of landslides	Total number during study period (2014–2018)	1.09 (0.34)	1.18 (0.48)	0.023
	Average number per year since first landslide during study period (2014–2018) (landslides per year since first during study period)a	0.55 (0.38)	0.78 (0.36)	$<0.001$
Time since last landslide exposure	Years since last exposure within the study period (2014–2018)	3.01 (1.78)	1.92 (1.45)	$<0.001$
Severity of exposure (0–10 scale)	Personal landslide exposure (cumulative for all landslides during the study period)b	4.80 (2.76)	2.21 (2.08)	$<0.001$
	Average personal landslide exposure (average across all landslides during the study period)	4.50 (2.42)	1.78 (1.33)	$<0.001$

Note: $p$-Values are for $t$-tests. SD, standard deviation.

^aThis value reflects the number of landslides a person experienced each year of the study period (2014–2018) after their first landslide exposure. For example, if a person first experienced a landslide in 2015, it would be the average number of landslides between 2015 and 2018.

^bPersonal landslide exposure is a composite score used to assess the severity of exposure. Values range from 0–10, where higher scores indicate more severe exposures.

Table 3 shows the displacement characteristics for those who relocated after landslides ($n=240$). The average number of displacements over the course of the 5-y study period for those who were displaced was 1.77 ($\pm 0.88$). On average, participants who were displaced moved 0.89 ($\pm 0.71$) times per year during the study period, and each move was on average 3.15 ($\pm 24.66$) km. Over half of the displaced respondents were still unstably housed at the time of the interview (59.6%), meaning that they were living in temporary housing or staying with friends or family members. Respondents spent an average of 5.48 ($\pm 5.28$) months in unstable housing over the course of the 5-y study period. On average, 64.0% of all moves (including when individuals moved multiple times) were made with the community (these moves were organized by the government, with additional support from other external donors).

Table 3.

Displacement characteristics among those who were displaced after landslides in Banjarnegara, Indonesia, between 2014 and 2018 ($n=420$).

Displacement characteristic	Description	Mean (SD) or %
Number of displacements	Total number of moves over the course of the study period, 2014–2018	1.77 (0.88)
	Average number of moves per year since first landslide during study period (2014–2018)a	0.89 (0.71)
Distance	Average distance per move (km)	3.15 (24.66)
Unstable housing	Unstably housed at time of the interview (percentage of respondents)	59.6
	Time spent in unstable housing over the course of the study period, 2014–2018 (months)	5.48 (5.28)
Community relocation	Moves made with other members of the community percentage of moves)	64.0

Note: SD, standard deviation.

^aThis value reflects the number of displacements a person experienced each year of the study period (2014–2018) after their first landslide exposure. For example, if a person first experienced a landslide in 2015, it would be the average number of displacements between 2015 and 2018.

Table 4 shows descriptive statistics for perceived changes in mental well-being (comparing before and after landslides) for both displaced and not displaced persons. A higher percentage of displaced persons reported feeling more economically stable (32.1% vs. 13.9%, $p<0.001$), more religious (79.6% vs. 72.8%, $p=0.093$), safer (61.2% vs. 27.2%, $p<0.001$), more optimistic (48.8% vs. 21.1%, $p<0.001$), closer with their family (57.1% vs. 56.1%, $p=0.978$), and closer with their community (55.0% vs. 51.2%, $p=0.184$) (comparing perceived change of well-being before and after exposure to landslides during the 5-y study period) compared with those who were not displaced. More displaced persons than nondisplaced persons perceived a change in their economic conditions post-landslide, with 41.1% saying they were less economically stable and 32.1% reporting they were more economically stable. In contrast, 52.2% of nondisplaced persons perceived no change in their economic conditions, and only 13. 9% felt more economically stable. Interestingly, small percentages reported feeling less close to their community (6.7% of movers and 2.8% of nonmovers) or family (2.1% of movers and 2.2% of nonmovers), or feeling less religious after the landslide (0.8% of movers and 0% of nonmovers). Because of these small numbers, we were unable to explore the odds of becoming less religious, less close with family, or less close with community after landslide-exposure; therefore, we focused only on perceived increases (e.g., becoming more religious after the landslide) after landslides compared with no change.

Table 4.

Mental well-being after landslide exposure in Banjarnegara, Indonesia, comparing those who were displaced and those who were not displaced by landslides. Responses [$n$ (%)] are for how the participant ($n=420$) evaluated change in their well-being comparing before and after exposure to landslides during the 5-y study period (2014–2018).

Categories of well-being	Displaced ($n=240$)	Not displaced ($n=180$)	$p$-Value
Economic stability			$<0.001$
Less stability	101 (42.1)	61 (33.9)
No change	62 (25.8)	94 (52.2)
More stability	77 (32.1)	25 (13.9)
Religiosity			0.093
Less religious	2 (0.8)	0 (0.0)
No change	47 (19.6)	49 (27.2)
More religious	191 (79.6)	131 (72.8)
Feelings of safety			$<0.001$
Less safe	63 (26.2)	93 (51.7)
No change	30 (12.5)	38 (21.1)
More safe	147 (61.2)	49 (27.2)
Optimism			$<0.001$
Less optimistic	41 (17.1)	51 (28.3)
No change	82 (34.2)	91 (50.6)
More optimistic	117 (48.8)	38 (21.1)
Closeness with family			0.978
Less close with family	5 (2.1)	4 (2.2)
No change	98 (40.8)	75 (41.7)
Closer with family	137 (57.1)	101 (56.1)
Closeness with community			0.184
Less close with community	16 (6.7)	5 (2.8)
No change	101 (42.1)	76 (42.2)
Closer with community	123 (51.2)	99 (55.0)

Note: Percentages may not sum to 100% owing to rounding. $p$-Values are for ${\mathrm{\chi }}^2$ tests.

Psychometric Properties of Community-Based Measure of Mental Well-Being

We next assessed the psychometric properties of our novel survey measure for perceived change in mental well-being. Perceived changes in each of the six variables (religiosity, closeness with family, closeness with community, economic stability, optimism, and sense of safety), comparing before and after landslide exposures, were only moderately correlated with one another (Figure 1). The highest correlations were between perceived changes in closeness with family and closeness with community (0.79) and sense of safety and optimism (0.53). Perceived change in religiosity was the least correlated with all other measures, particularly with economic stability (0.01) and sense of safety (0.06).

Figure 1.

Correlation (Kendall rank correlation coefficient) between the measures of perceived changes in mental well-being ($n=420$).

A two-factor structure emerged from the exploratory factor analysis that collectively accounted for 49.96% of the variance in the responses. These results are presented in Table 5 and show perceived changes in closeness with family and community loading onto the same factor, which we called “interpersonal dimensions” of mental well-being. Perceived changes in economic stability, sense of safety, and optimism about the future loaded onto the same factor, which we labeled “practical dimensions” of mental well-being. We tested reliability across all six measures of mental well-being by calculating Cronbach’s alpha coefficient. The alpha value was 0.66 [95% confidence interval (CI): 0.61, 0.71], indicating a fair degree of internal consistency (Ponterotto and Ruckdeschel 2007), but it was below the commonly preferred cutoff of 0.70 (Bland and Altman 1997; Tavakol and Dennick 2011). The internal consistencies for Factors 1 and 2 were stronger with Cronbach’s coefficients of 0.85 (95% CI: 0.83, 0.88) and 0.71 (95% CI: 0.66, 0.76), respectively, indicating stronger (“excellent” and “moderate”) internal consistency (Ponterotto and Ruckdeschel 2007).

Table 5.

Factor loadings and cumulative percentage variance explained by the factors on the community-based mental well-being measure, for participants who experienced landslides in Banjarnegara, Indonesia, between 2014 and 2018 ($n=420$).

Factor	Factor label	Percentage variance explained (cumulative)	Variable loadings
			Closeness with family	Closeness with community	Economic stability	Sense of safety	Optimism about the future	Religiosity
1	Interpersonal dimensions	26.10	0.747	0.977	—	—	—	—
2	Practical dimensions	49.96	—	—	0.518	0.705	0.792	—

Note: —, not applicable.

The resulting “interpersonal dimensions” score is a composite score ranging from $-3.17\text{\hspace{0.17em}to\hspace{0.17em}}1.18$, where increases in the scale represent increases in perceived changes in interpersonal well-being, and the “practical dimensions” score is a composite score ranging from $-2.07\text{\hspace{0.17em}to\hspace{0.17em}}1.72$, where increases in the scale represent increases in perceived changes in practical dimensions of well-being.

Results from the Analyses

The effect of displacement on the association between landslides and mental well-being.

We assessed the effect of displacement on the association between landslides and change in perceived mental well-being using six separate models, one for each measure of mental well-being. Covariates were only moderately correlated (Spearman’s rank correlation coefficient for all variables was $<0.6$). The main effects from each model are shown in Figure 2. The full models with all covariates are shown in Table S1.

Figure 2.

Estimated association between landslide-displacement and perceived change in mental well-being in Banjarnegara, Indonesia, between 2014 and 2018. Each dot-and-whisker represents a separate logistic or multinomial regression model for the estimated odds ratio (95% confidence intervals) of reporting increases in the respective community-based measure of mental well-being after landslides compared with no change. Each model was adjusted for landslide characteristics (number of landslides, personal landslide exposure, and time since last exposure) and sociodemographic characteristics (age, sex, level of education, income, and employment in agriculture). Sample sizes for each model are provided with full results in Table S1.

We found that displacement was associated with perceived increases in mental well-being after exposure to landslides during the study period, meaning that those who were displaced after a landslide were more likely to report improved mental well-being, comparing periods after to before the landslide, than those who were not displaced after landslide exposure. Displaced persons were more likely to perceive increased economic stability after exposure to landslides compared with those who were not displaced [$\text{odds ratio\hspace{0.17em}}\left(\text{OR}\right)=3.06$ (95% CI: 1.45, 6.46)] times. They were $\text{OR}=4.01$ (95% CI: 1.87, 8.61) times as likely to report feeling more optimistic, $\text{OR}=2.71$ (95% CI: 1.44, 5.10) times as likely to feel safer, $\text{OR}=1.92$ (95% CI: 1.03, 3.65) times as likely to feel more religious, and $\text{OR}=1.90$ (95% CI: 1.10, 3.33) times as likely to feel closer to their community than those who were not displaced. The sensitivity analysis using sandwich estimators for standard errors indicated that results were generally robust to possible geographic clustering (Figure S1). However, the odds of increased religiosity (comparing displaced to not displaced persons), which was statistically significant in our primary analysis, became only borderline significant after calculating robust standard errors ($\text{OR}=1.92$; 95% CI: 0.97, 3.81).

We also conducted a sensitivity analysis to assess the effect of displacement on the association between landslides and the two factors that emerged from the exploratory factor analysis. The results using these two factors were broadly consistent with our main analysis: Being displaced was significantly associated with a 0.30-point increase ($p=0.020$) in the interpersonal dimensions scale and with a 0.54-point increase in the practical dimensions scale ($p<0.001$). Results are presented in Table S2.

We next introduced interaction terms between displacement and each sociodemographic characteristic in each model. Across all models, only the displacement–income interaction was significant (economic stability, $p<0.001$; optimism, $p<0.001$; sense of safety, $p<0.001$; religiosity, $p=0.005$; closeness with family, $p=0.023$; and closeness with community $p=0.021$). The $p$-values for all interaction terms are presented in Table S3. To investigate this further, we stratified each model by displacement (yes/no) and found that the association between earning less than the minimum wage and a perceived change in well-being after landslides (Figure 3) was different between displaced and not displaced persons (sample sizes are shown in Table S4). Among displaced populations, we did not observe evidence of an association between earning less than the minimum wage and a change in mental well-being after landslides. However, among those who were not displaced, earning less than the minimum wage was associated with a reduced odds of reporting perceived increases in sense of safety ($\text{OR}=0.07$; 95% CI: 0.02, 0.20), religiosity ($\text{OR}=0.13$; 95% CI: 0.03, 0.47), optimism about the future ($\text{OR}=0.03$; 95% CI: 0.01, 0.13), and economic stability ($\text{OR}=0.04$; 95% CI: 0.01, 0.16) after landslides.

Figure 3.

Association between earning less than the minimum wage on change in perceived mental well-being after landslides, stratified by displacement after landslides in Banjarnegara, Indonesia, between 2014 and 2018. Each dot-and-whisker represents a separate logistic or multinomial regression model for either nondisplaced or displaced persons and the estimated odds ratio (95% confidence interval) of reporting increases in the measure of mental well-being after landslides. Each model was adjusted for landslide characteristics (number of landslides, personal landslide exposure, time since last exposure) and sociodemographic characteristics (age, sex, level of education, income, and employment in agriculture).

Mobility patterns.

In secondary analyses, we explored who was most likely to move after landslide exposure (based on age, sex, level of education, household income, or employment in agriculture), as well as how different patterns of mobility (time spent in temporary housing, the distance of displacement, whether a person was unstably housed at the time of the interview, and the number of times they were displaced) were associated with perceived changes in mental well-being after landslides compared with before. These results are presented in Table 6.

Table 6.

Associations [adjusted ORs (95% CIs)] between sociodemographic and exposure characteristics and displacement after landslides in Banjarnegara, Indonesia, between 2014 and 2018 ($n=405$).

Categories	Variables	Odds ratio of being displaced vs. not displaced
Landslide characteristics	Number of landslides	0.12 ($0.05,0.28$)
	Personal landslide exposurea	1.94 ($1.65,2.29$)
	Time since last exposure (0–4 y)	1.60 ($1.35,1.89$)
Demographic characteristics	Age group (y)
	$<31$	Ref
	31–43	0.84 ($0.40,1.76$)
	44–54	0.42 ($0.19,0.93$)
	$\ge 55$	0.59 ($0.24,1.44$)
	Level of education
	No educationb	Ref
	Primary school	0.84 ($0.39,1.82$)
	More than primary school	0.81 ($0.32,2.05$)
	Female vs. male	0.97 ($0.56,1.71$)
	Earning less than the minimum wage (Y/N)	0.53 ($0.29,0.97$)
	Employed in agriculture (Y/N)	1.10 ($0.58,2.07$)

Note: ORs and 95% CIs were estimated using logistic regression model fully adjusted for all covariates shown in the Table. CI, confidence interval; N, no; OR, odds ratio; Ref, reference; Y, yes.

^aPersonal landslide exposure is a composite score used to assess the severity of exposure. Values range from 0–10, where higher scores indicate more severe exposures.

^bNo education or less than primary school level of education.

As Table 6 indicates, those who earned less than the minimum wage were 0.53 (95% CI: 0.29, 0.97) times as likely to relocate after a landslide than those earning more than the minimum wage. Further, the odds of being displaced after a landslide increased with more severe landslides (as indicated by higher scores on the personal landslide exposure scale). For each additional personal landslide exposure on the scale from 0–10, the odds of displacement after landslides increased 1.94 times (95% CI: 1.65, 2.29). In contrast, for every additional landslide a participant experienced during the study period, they were 0.12 (95% CI: 0.05, 0.28) times as likely to be displaced. Finally, for each additional year since the exposure, the odds of displacement increased by 1.60 times (95% CI: 1.35, 1.89).

Lastly, we explored the association between move-based characteristics (time spent in temporary housing, distance of displacement, whether a person was unstably housed at the time of the interview, and whether they received assistance to relocate or not) and perceived changes in mental well-being among those who were displaced by landslides ($n=240$). Results are presented in Table 7. We found that those who were living in unstable housing at the time of the interview were less likely to report perceived increases in mental well-being after landslides: They were 0.26 (95% CI: 0.11, 0.62) times less likely to report perceived increased economic stability, 0.14 (95% CI: 0.04, 0.48) times less likely to have increased optimism, and 0.17 (95% CI: 0.06, 0.52) times less likely to report increased sense of safety after exposure to landslides during the 5-y study period than before exposure to landslides. The odds of reporting perceived increases in religiosity, closeness with family, or closeness with community after landslides were not significantly associated with any of the move-based characteristics (e.g., distance, moving with other community members, being unstably housed at the time of the interview, or time spent in unstable housing) (Table S5).

Table 7.

Estimated association between move-based characteristics on the ORs (95% CIs) of reporting perceived changes in economic security, sense of safety, and optimism about the future among movers after landslides in Banjarnegara, Indonesia, between 2014 and 2018.

Variables	More economically stable vs. no change ($n=228$)	More optimistic vs. no change ($n=228$)	More safe vs. no change ($n=228$)
Number of displacements (1 displacement increase)	0.82 (0.49, 1.36)	0.64 (0.35, 1.17)	1.20 (0.67, 2.16)
Distance ($\mathrm{1}\text{-}\mathrm{km}$ increase)	0.83 (0.64, 1.06)	0.74 (0.56, 0.96)	0.94 (0.84, 1.06)
Unstably housed at time of the interview (Y/N)	0.26 (0.11, 0.62)	0.14 (0.04, 0.48)	0.17 (0.06, 0.52)
Time spent in unstable housing (1-month increase)	1.02 (0.94, 1.11)	1.12 (1.01, 1.25)	1.09 (1.00, 1.19)
Move made with other community members (Y/N)	1.64 (0.67, 4.01)	1.07 (0.37, 3.07)	0.65 (0.26, 1.61)

Note: Odds ratios were estimated using multinomial regression models. Each model adjusted for landslide characteristics (number of landslides, personal landslide exposure, time since last exposure) and sociodemographic characteristics (age, sex, level of education, income, and employment in agriculture). CI, confidence interval; N, no; OR, odds ratio; Y, yes.

Discussion

The main finding of this study was that individuals who relocated within the Banjarnegara region after landslide exposure were more likely to report improvements in perceived well-being after landslides than those who experienced landslides but did not relocate. When asked to compare perceived economic stability before and after landslide exposure, those who were displaced were more likely to report increases in economic stability than those who were not displaced. Displaced persons were also more likely to report perceived increases in optimism, sense of safety, religiosity, and closeness to community after landslides compared with before than those who experienced landslides but were not displaced. These results suggest that those who moved away after landslides tended to see improved outcomes compared with those who stayed behind.

These findings differ from those of previous research on other natural disasters, which suggests that displacement is often associated with increased risk of adverse psychological outcomes over the long term (from 1 to $>10\mathrm{y}$ after a disaster) (Arcaya et al. 2020). A 2009 literature review found that 19 of 20 studies on disasters, displacement, and mental health showed either increased risk among displaced persons or null results; the outcomes studied varied, including depression, PTSD, distress, and psychosomatic symptoms (Uscher-Pines 2009). More recent studies have found similar results, reporting an increased risk of adverse mental health outcomes among those who were displaced by tropical cyclones (Schwartz et al. 2018a, 2018b) and flooding (Munro et al. 2017). In contrast, research in related fields has found that displacement may be associated with increased opportunities for economic development (Deryugina et al. 2018) and education for one’s children (Stillman et al. 2009). Some have suggested that these increased opportunities may mediate negative mental health outcomes among displaced persons (Manove et al. 2019; Raker et al. 2019). For example, other work on migration (not in the context of disasters) has noted that moving can be a stressful process, but that mental health can also improve for those who move (Stillman et al. 2009). One challenge of comparing across the literature is that studies use a wide range of analytic approaches, control groups, and outcome definitions, in addition to the inherent differences among types of environmental disasters and populations (e.g., there is a different ability to respond to disasters among industrialized nations).

Our findings may differ from those in earlier work for a number of reasons. First, we used locally specific metrics for well-being, which makes comparability to other studies difficult. Second, we focused on perceived changes in mental well-being, rather than using objective outcome measures. Third, in contrast to this study, much of the literature on environmental-displacement and health has focused primarily on persons living in industrialized nations (Munro et al. 2017; Schwartz et al. 2018a). Although some studies have considered low-income populations within these high-income countries (Fussell and Lowe 2014), fewer studies have considered low-income populations in nonindustrialized (or newly industrialized) countries (Loebach and Korinek 2019). Fourth, this is the first study (to our knowledge) to quantitatively assess the impact of landslide-displacement [our earlier work used qualitative approaches to investigate these relationships (Burrows et al. 2021)]. Landslides are extremely sudden-onset events that often occur with little warning and may cause significant damage (Kennedy et al. 2015). It is therefore possible that landslides have different impacts on displacement and mental well-being than other previously studied environmental exposures. Future research on landslide-displacement should be conducted to confirm these findings.

Secondary findings showed that household income was positively associated with changes in perceived mental well-being after exposure to landslides compared with before exposure to landslides, but only among those who did not relocate after landslide exposure. This suggests that displacement modifies the association between household-level income and mental well-being after landslides. Among those who did not move after landslide exposures, low-income populations fared more poorly than their higher-income counterparts. Among those who did move after landslides, we saw no association in change in perceived well-being in relation to income. One possibility for these findings is that some of the nonmovers in our study may have wanted to move but been unable to do so because of financial limitations or other familial or social ties that restricted their mobility. This is supported by our observation that those earning less than the minimum wage were less likely to relocate than those earning more than the minimum wage. Historically, relocation after natural disasters has been viewed as a last resort, which assumed that all exposed persons would prefer to stay in situ if possible (Tacoli 2009). However, our findings indicate that in certain cases, mobility may be a successful form of adaptation to adverse environmental events, particularly among low-income populations. Mobility as an adaptive strategy to climate change has been discussed at length conceptually, although the primary focus has been on planned relocation in the context of slow-onset climate change (Adger 2014; Black et al. 2011b; Tacoli 2009). Our study contributes to a limited body of empirical research that suggests that, in some cases, relocation can have a beneficial impact on health and well-being following sudden-onset disasters (Muir et al. 2019; Najarian et al. 2017).

Finally, we found that individuals who were still living with friends or family or in temporary housing at time of their interview were less likely to report positive perceived changes in mental well-being after landslide exposures compared with before landslide exposure. This is consistent with extant research that shows an increased risk of adverse mental health outcomes among those who were unstably housed after floods (Munro et al. 2017) and hurricanes (Fussell and Lowe 2014). Similar associations have been observed outside of the context of disaster displacement: Homelessness and living in unstable housing are associated with adverse mental health worldwide (Padgett 2020). In contrast, we found that whether or not a person relocated with other members of their community was not significantly associated with mental well-being. Sixty-four percent of displaced persons moved as part of government- or donor-supported relocations with their whole community. In these moves, participants received free or subsidized housing, but they did not have control over where they would live. These findings are consistent with our previous qualitative research in this community, in which participants described that even when they moved with other members of the community, they still felt strains on their well-being and community cohesion (Burrows et al. 2021). Participants attributed this to the death of some community members during the landslide and to missing the physical place and land they used to inhabit (Burrows et al. 2021). Although whole-community relocation has been shown to help maintain social networks and community cohesion (Hikichi et al. 2017), the findings from this study, and our qualitative research, suggest that whole-community relocation does not guarantee community cohesion or improved well-being. This has important implications for planned relocation in the context of climate change, and suggests that supporting relocation through housing development may not be sufficient to preserve mental well-being. The role of community-level dynamics after environmentally driven displacement should be explored more fully in future research, with particular attention to the impact of government-supported relocation on population health and well-being.

Although our sampling approach was appropriate for this study of a mobile population and was designed with our local partners, the use of nonrandom sampling is a limitation. For example, nonrandom sampling could result in inadvertent oversampling in displaced communities who were more well-settled and therefore more likely to report improved perceived well-being. However, we believe this is unlikely given that more than half of the displaced respondents were still unstably housed at the time of data collection (either living in informal settlements or staying with family or friends). In addition, although the convenience-based sampling procedure employed in this study allowed us to capture participants who relocated across Banjarnegara, we did not track possible participants who relocated outside of Banjarnegara. This means our results may not be generalizable to individuals who relocate over longer distances after landslides, who may differ in important ways from those who stay in place or who relocate locally. However, the majority of environmentally driven relocation seems to occur over relatively short distances (Roland and Curtis 2020). The generalizability of this study is also impacted by the fact that the majority of respondents were female (65.0%). This may reflect the fact that women in Banjarnegara were easier to access than men and that during the snowball sampling process women more readily provided us with the names of other women to survey.

An additional limitation is that we collected retrospective data, which could result in recall bias. Participants may have reported differential landslide exposures or mental well-being based on displacement status. However, although we did find that those who were displaced were more likely to report more severe landslide exposures, we also found that those who were not displaced reported more landslide exposures overall. Further, we were not able to assess whether participants’ perceived well-being changed as they settled into their post-landslide realities over time. All the participants included in this study experienced landslides within 5 y of data collection (on average the last landslide exposure was 3.01 y before data collection for displaced persons and 1.92 y before data collection for not displaced persons). It is possible that some of the reported improvements in well-being might be temporary and that our findings would have been different if we had sampled participants 10 or 15 y after landslide exposure. Delayed departure may also confound some of our observations (Arcaya et al. 2020; Fussell et al. 2010). The descriptive statistics indicated that those who did not relocate experienced more recent landslides compared with those who did move, and our adjusted analysis showed that for each additional year since their last exposure, individuals were more likely report moving because of the landslide (Table 6). This suggests that some persons who were nonmovers at the time of the interview might eventually move in the future. For other environmental exposures, the impact on the migration appears to vary over time [e.g., in some contexts, drought is associated with reduced mobility in the short term but with increased mobility in the following years (see review by Hunter et al. 2013)], suggesting that future studies would benefit from longitudinal data to assess temporal patterns in landslide-mobility.

Despite these limitations, this project provides an important contribution to the literature. To our knowledge, this is the first study to explore the effect of landslide-displacement on mental well-being. Our results provide important information for local communities who are grappling with more frequent landslides and have significant implications for policy makers. A key innovation of this project is the use of a locally developed metric for mental well-being. Our approach incorporated the community into the process of designing the questionnaire and centered the study on the mental health experience of that community, rather than using measures based on Westernized medical ideas. Overall, this measure represents a novel tool to assess mental well-being in Banjarnegara. The construct of mental well-being is not universally defined, which is reflected in the range of tools used to assess mental well-being (Rose et al. 2017). Some frameworks divide mental well-being into two dimensions (hedonic, or emotional, well-being and eudaimonic, which includes personal functioning and social well-being) (Ryan and Deci 2001), and others propose three constituents (emotional, psychological, and social well-being) (Keyes 2002). The community-derived measure presented here is broadly consistent with these frameworks, but the individual items reflect the priorities and worldview of the studied community. For example, closeness with community and family reflect social or interpersonal well-being and have been included in other measures for mental well-being (Rose et al. 2017). However, in contrast with other scales, members of the Banjarnegara community also identified economic stability as an essential component of mental well-being. Economic factors are often included in overarching well-being frameworks (Lindert et al. 2015) and are associated with mental health (Syrén et al. 2020), but they are not commonly incorporated as central components of mental well-being (Rose et al. 2017). This underscores the need for locally defined metrics that allow communities to self-identify which dimensions of an abstract concept, such as mental well-being, are important in their own unique cultural context. Given the transcultural nature of this study, involving the community in the development of the questionnaire allowed the research to be more collaborative and meaningful to the participants (Parker and Kingori 2016; Reynolds and Sariola 2018).

Another key innovation of this study is the focus on perceived change in mental well-being comparing before and after landslide exposure. In contrast, much of the research on disaster displacement assesses mental health at the time of the interview, rather than changes in mental health before and after disaster displacement (Munro et al. 2017; Uscher-Pines 2009). Further, our focus on the perception of change, rather than absolute change, allowed us to incorporate differing expectations about what constitutes good well-being. By asking people to describe their well-being relative to how they felt before the event, we were able to add a temporal aspect to our study.

Our findings have important implications for the Banjarnegara community, for policy makers, and for researchers who study environmental-mobility and mental well-being. Over the last decade, medical professionals have advocated for better integration of mental health care interventions into emergency medicine and disaster preparedness plans (Math et al. 2015; North and Pfefferbaum 2013; Yun et al. 2010). Our results support these calls but provide new evidence that could guide more targeted and effective mental health care support. Specifically, the increased odds of reporting improvements in mental well-being among displaced persons (compared with those who were not displaced) suggests that in Banjarnegara those who stayed behind after landslides might benefit most from increased mental health support. Among those who did move, periods of unstable housing may be key intervention points for improving mental health. These findings can inform resource allocation and recovery programming after landslides in Indonesia.

This project can also be used to inform future research on weather- and climate-related mobility and mental well-being. Our community-centered approach could be used to improve policy recommendations through incorporation of local voices and locally specific outcome assessments. The use of local indicators for policy-making is becoming more common in environmental health (English et al. 2009; Gonzalez et al. 2011) and other related fields (Sterling et al. 2017a, 2017b). Similar locally relevant approaches may be applied in different communities and should focus specifically on the impact of environmental mobility on low-income populations. In addition, future research should employ longitudinal study designs to follow participants in the months and years after climate- and weather-related disasters to track mobility and the associated impacts over time. Longitudinal and repeated-measure studies will be particularly useful to understanding if and how mental health changes across different stages of recovery and rebuilding and will aid in better understanding the complex mechanisms between landslides, displacement, and mental well-being.

Longitudinal data will also allow researchers to better explore the associations between repeated exposure to climate- and weather-related disasters and mobility more fully. In this study, we found that people who were not displaced experienced more frequent but less severe landslides than those who were displaced. This could indicate that participants who experienced multiple small exposures were able to adapt in place and therefore did not need to relocate. However, given that we found improvements in perceived well-being among displaced persons, this may also suggest that those who did not relocate were unable to move away from unsafe areas and therefore experienced more repeated landslide exposures. We were unable to explore these questions further owing to data limitations; however, the impact of repeated exposures on population mobility and health remains an important area of future study, particularly in the context of climate change because disasters are expected to become more frequent. Future research should also consider longer-distance and cross-border mobility, which may have different and unique impacts on mental health and well-being. This may require the development of novel methods to identify and access environmentally displaced persons.

Finally, international policy on weather- and climate change-related mobility should more explicitly recognize the needs of immobile populations [those who do not move—either voluntarily or due to a desire to stay in place (Schewel 2020)]. The focus to date has largely been centered on individuals who move because of the environment. For example, the Intergovernmental Panel on Climate Change mentions nonmovers briefly in its section on climate-related migration but does not discuss the potential health disparities related to immobility (Adger 2014; Smith et al. 2014). Our results indicate that this is a gap that should be addressed with policy to protect those who stay behind after natural disasters. However, in addition to protections for immobile populations, improved protections for displaced persons are still needed. Although in this study, displaced persons were more likely to report improvements in perceived well-being after landslides (compared with before) than those who were not displaced, this does not mean that displaced persons were insulated from the adverse impacts of landslide exposure. For example, despite reporting improvements in perceived economic stability, our descriptive statistics show that nearly half of displaced households earned below the minimum wage at the time of data collection. Thus, although the findings presented in this paper highlight the need for improved policy and support focused on those who remain behind after disasters, it is essential that any such steps are taken in parallel with improved protections for those who are displaced by disasters.

This study contributes to an important and growing body of literature on environmental-mobility and health by focusing on landslides in Indonesia, an understudied natural disaster in a high-risk and low-income population. We found that those who relocated were more likely than their immobile counterparts to perceive increases in economic stability, optimism, sense of safety, sense of closeness with the community, and religiosity after landslides (compared with before landslides). These findings may have important implications for both policy makers and the Banjarnegara community related to who is most vulnerable after landslides.