Edited by: Sara Carletto, University of Turin, Italy
Reviewed by: Long Sun, Shandong University, China; Berta Rodrigues Maia, Catholic University of Portugal, Portugal
This article was submitted to Health Psychology, a section of the journal Frontiers in Psychology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
The term debt can be understood in two different but related concepts, one as a specific legal instrument that connects lenders to debtors, and the other as having less assets to liabilities (Charron-Chénier and Seamster,
Arandjelovic et al. (
In terms of depression, the amount of debt is not the sole predictor of depression. It is found that among the older adults in Japan, having debt was significantly related to the increase in mild–moderate and severe depression; this is attributed to the obligation to repay debt results in psychological pain or reduced quality of living conditions (Tatsuhiko et al.,
In studies of anxiety, Dackehag et al. (
Additionally, there have been numerous past studies on the effect of debt on stress. Norvilitis et al. (
In terms of suicide behavior, although not observed as a statistically significant pattern, debt was mentioned as a relevant factor for suicide behavior in 11% of men by inquest witnesses (Scourfield et al.,
Higher debt/income ratio is significantly related to worsening health and self-reported health with health behaviors and risk explaining part of the association between debt, stress about debt, and health (Drentea and Lavrakas,
From these past studies, there is evidence to support a need for a deeper understanding of how debt affects the mental health of individuals. One such method is through a systematic review, a form of review that involves a thorough and comprehensive plan using a search strategy with the aim of lowering bias by locating, evaluating, and synthesizing all relevant studies on the studied topic (Uman,
Based on previous literature, it can be derived that debt plays an important part in the mental and physical health of humans. However, there are conceptual and cultural issues to be addressed in reviewing literature on debt and mental health across cultures. First, in terms of conceptual definitions of debt and measurements of debt—the use of clear definition of debts and measures of multidimensional domains of debt may facilitate the accuracy in measuring debt or loan. The use of multidimensional domains of debt are better compared to the use of single-item response to measure debt (Roth et al.,
This review follows the PRISMA guidelines, and the PRSIMA flowchart was also adapted to summarize the search process (Moher et al.,
Five databases were searched. These are Medline, PubMed, Web of Science, Scopus, and ScienceDirect. Generally, a systematic review requires the use of more than two databases and should go beyond the use of MEDLINE database (Charrois,
The search terms used were debt* or indebtedness or over-indebtedness or credit or loan or “financial problems” or bankrupt and Asia and “mental disorder” or “mental health” or “mental illness” or depression or anxiety or stress or suicide or “suicide ideation.”
The EBSCOhost search engine was used for the MEDLINE database search, and the following limiters were used as they were ready-made and to enhance accuracy of search results: Age set to 19–44 years old, and geography set to India, China, Malaysia, Japan, Thailand, Bangladesh, and Republic of Korea.
Third, the published journal article discusses the relationship between problematic and non-problematic debt on depression, anxiety, suicide or suicide ideation, or stress. The study includes studies on participants from Asia only.
The following exclusion criteria were followed. First, the manuscript was excluded if the content was not available in English. This limiter is considered acceptable as research finds no bias in systematic reviews of meta-analysis of conventional medicine that apply the language restriction (Morrison et al.,
It was decided that the review would focus on published journal articles, as it was judged to be biased if books and thesis or dissertations on the subject matter were selected over the other due to their unavailability via online search and written language.
Each accepted manuscript for review was analyzed through a systematic and careful process. The full text of the articles was read, exploring their methodology and results. Information on the study's design, sampling method, sample size, psychological tools used, and definition of debt was recorded. Additionally, results on the relationship between debt and depression, anxiety, suicide, and suicide ideation were noted. All relevant findings are categorized and presented in a descriptive method in
Result Table.
Kaufman ( |
Quantitative cross sectional | Purposive sampling method | 1. Questionnaire developed from interview results conducted by researcher in a prior research and the National Institute of Health (North America). |
Debt is measured by the severity level of loans the participant has. | Non-organic farmers in Ubon Ratchathani were significantly happier than organic farmers. However, both organic and non-organic farmers experience depression, or sadness in relation to their debts. | |
Lee et al. ( |
Quantitative, longitudinal study | Stratified sampling | 1. The Center for Epidemiologic Studies Depression Scale (CESD-11) |
Debt was defined by ratio of interest paid in household debts to disposable income. | After adjusting for covariates, the higher the house-related interest to disposable income ratio people with houses had, the higher the depression scores. In the middle-low equalized income group, people with over 10% house-related interest to disposable income ratios had significantly higher depression scores than people without houses when setting people with houses and no debts as the reference. In the low-income group, regardless of house possession or related interest status, people had noticeably higher depressive symptoms than individuals in other income groups except for people in the under 5% house related interest group | |
Manning et al. ( |
Quantitative cross sectional | Purposive sampling | 1. The Addiction Severity Index-Lite (ASI-Lite) |
Debt was not defined. | Participants with debt were 1.9 times more likely to report suicidal thoughts in the past month, 1.6 times more likely to report a suicidal plan, and were 1.6 times more likely to attempted suicide. | |
Maselko et al. ( |
Quantitative, cross sectional. | Cluster randomization | 1. Patient Health Questionnaire (PHQ-9) | Debt was measured as a Yes/No/Unknown question whether the household was in debt. | Being in a family in debt was associated with a 2.08-point higher PHQ-9 score. Debt continued to independently predict depression symptoms together with the asset index in the current study. There was weak evidence that the association between debt and depression symptoms was stronger among those toward the bottom of the asset score distribution, although this difference did not reach statistical significance (results not shown). | |
Mathias et al. ( |
Quantitative cross sectional | Randomized cluster sampling. | 1. Patient Health Questionnaire (PHQ9) |
Debt is not well defined, but is implied is the loans participants take in the last 6 months. | Depression prevalence among people who had taken a recent loan was thrice that of those who had not. | |
Seponski et al. ( |
Quantitative cross sectional | Multi stage Cluster sampling | 1. Harvard Trauma Questionnaire (HTQ) |
Debt is not defined. | Respondents who were in debt had the highest percentage of anxiety (35.26%). Similarly, respondents who were in debt had the highest percentage of depression (27.75%) significant at ( |
|
Sharma et al. ( |
Quantitative cross sectional | Purposive sampling | 1. Modified Caregiver Stress Index (MCSI) |
How debt is measured is not defined. However, this research refers to debt due to illness. | Caregiver strain was found to be high and statistically significant with being in debt. | |
Shidhaye et al. ( |
Quantitative cross sectional | Systematic random sampling | 1. Marathi version of Patient Health Questionnaire (PHQ-9). |
No definition of debt but implied it refers to loan. | The risk of depression was one and half times in individuals belonging to households below poverty line and it was double in those who were in debt. | |
Xu et al. ( |
Quantitative, cross sectional | Cluster sampling | 1. Question about suicidal ideation (Yes/No) in the past 12 months. |
Debt is measured as a Yes/No question. | Being in debt is significantly positively associated with suicidal ideation, for women being in debt is related to suicide ideation within 12 months. |
The characteristics and culture background of participants.
Kaufman ( |
Thailand | Rural | 22.7% earning 0–40,000 Baht. | 50.7% with 6th grade or less education. | Distribution by ethnic group is not reported. |
12.0% earning 41,000–60,000 Baht. | 49.3% with 7th grade or more education. | ||||
28.0% earning 61,000– 100,000 Baht. | |||||
21.3% earning 101,000–200,000 Baht. | |||||
16.0% earning 201,000 or more. | |||||
Lee et al. ( |
Korea | Rural and non-rural | 24.1% households with low income. | 41.3% with less than high school education. | Distribution by ethnic group is not reported. |
24.3% households with middle low income | 30.8% with high school graduate education. | ||||
25.6% households with middle high income. | 27.8% with college graduate education. | ||||
26.0% households with high income. | |||||
Manning et al. ( |
Singapore | Not reported | Participants' income is not reported. | 68.7% educated to secondary school or above. | 61.3% Chinese |
9.7% Indian | |||||
5.6% Malay | |||||
3.4% are of other races. | |||||
Maselko et al. ( |
Pakistan | Rural | Participant's income is not reported. Study uses an asset index score as a measure of socioeconomic status. | 18.8% women with no education. | Distribution by ethnic group is not reported. |
24.4% women with primary education. | |||||
18.8% women with middle education. | |||||
22.1% women with higher secondary education. | |||||
8.1% women with higher secondary education. | |||||
7.9% with tertiary education. | |||||
9.6% men with no education. | |||||
11.8% men with primary education. | |||||
24.0% men with middle education. | |||||
42.6% men with secondary education. | |||||
8.2% with higher secondary education. | |||||
3.7% with tertiary education. | |||||
Mathias et al. ( |
India | Rural and non-rural | Participant's income is not reported. | 22.7% with none or incomplete primary education. | 25.4% Scheduled Caste/Tribe |
18.3% with primary completion. | 15.4% Other Backward Caste | ||||
41.5% with secondary completion. | 59.2% General | ||||
17.5% with graduate education. | |||||
Seponski et al. ( |
Cambodia | Rural and non-rural | Participant's income is not reported. | 18.1% no education | Distribution by ethnic group is not reported. |
47.6% primary school | |||||
21.7% secondary school | |||||
12.6% high school and beyond. | |||||
Sharma et al. ( |
Nepal | Not reported | Participant's income is not reported. | 36% caregivers of schizophrenia patients with education up to grade 12. | Distribution by ethnic group is not reported. |
64% caregivers of schizophrenia patients who graduated and above. | |||||
24% caregivers of bipolar affective disorder patients with education up to grade 12. | |||||
76% caregivers of bipolar affective disorder patients who graduated and above. | |||||
Shidhaye et al. ( |
India | Rural | In Dhamangaon | In Dhamangaon | In Dhamangaon |
12.0% first quintile annual income. | 6.5% graduated and above. | 56.9% other backwards caste. | |||
18.4% second quintile annual income. | 17.0% junior college. | 15.4% schedule caste. | |||
13.7% third quintile annual income. | 29.5% high school. | 11.3% schedule tribe. | |||
31.9% primary and middle school. | 16.3% general. | ||||
15.1% illiterate. | In Chandur Bazaar | ||||
52.9% other backwards caste. | |||||
23.1% schedule caste. | |||||
In Chandur Bazaar | 4.5% schedule tribe. | ||||
9.2% graduated and above. | 19.4% general. | ||||
30.1% fourth quintile annual income. | 12.0% junior college. | ||||
29.3% high school. | |||||
25.7% fifth quintile annual income. | 36.1% primary and middle school. | ||||
In Chandur Bazaar | 13.4% illiterate. | ||||
27.2% first quintile annual income. | |||||
27.2% second quintile annual income. | |||||
24.3% third quintile annual income. | |||||
14.0% fourth quintile annual income. | |||||
7.3% fifth quintile annual income. | |||||
Xu et al. ( |
China | Non-rural | Participants' income not reported. | Non-suicide ideation |
Distribution by ethnic group is not reported. |
The types, definitions and measurement of debts.
Kaufman ( |
Yes | Debt was defined measured as loan status over five years. | Participants self-report on a Likert scale, the higher the value the lower the loan. |
Lee et al. ( |
Yes | Debt was defined by ratio of interest paid in household debts to disposable income. | Participants answer three questions. First, what was their house ownership category? Second, at the end of last year how much house related debt interest was paid. Third, inquired on the household income. |
Manning et al. ( |
No | Debt was not defined. | Debt measuring method was not reported. However, data reported on debt is presented as a Yes or No response. |
Maselko et al. ( |
No | Debt was not defined. | Participants self-report on a single question on whether the household was in debt. Participants responses fell into categories of Yes, No or Unknown. |
Mathias et al. ( |
No | Debt was defined as loans in the last 6 months. | Participants self-report in a survey whether Yes or No they had loans in the last 6 months. |
Seponski et al. ( |
No | Debt was not defined. | Participants self-reported on a structured interview on their monthly saving per capita, participants responses fell into three categories; In debt, savings, or in debt and no savings. |
Sharma et al. ( |
Yes | Debt was referred as debt due to illness. | Participants self-reported in an interview whether they had taken or not taken debt due to illness. |
Shidhaye et al. ( |
No | Debt was not defined. | Participants self-reported Yes or No if they had loans. |
Xu et al. ( |
No | Debt was not defined. | Participants self-reported Yes or No if their family had any debts in the past year |
Summary of Risk of Bias.
1. Was the research question or objective in this paper clearly stated? | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
2. Was the study population clearly specified and defined? | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
3. Was the participation rate of eligible persons at least 50%? | Yes | NR* | Yes | NR* | No* | NR* | NR* | NR* | NR* |
4. Were all the subjects selected or recruited from the same or similar populations (including the same time period)? Were inclusion and exclusion criteria for being in the study prespecified and applied uniformly to all participants? | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
5. Was a sample size justification, power description, or variance and effect estimates provided? | No | No | No | No | Yes | Yes | No | Yes | No |
6. For the analyses in this paper, were the exposure(s) of interest measured prior to the outcome(s) being measured? | No | Yes | Yes | No | No | No | No | No | No |
7. Was the timeframe sufficient so that one could reasonably expect to see an association between exposure and outcome if it existed? | No | Yes | No | No | No | No | No | No | No |
8. For exposures that can vary in amount or level, did the study examine different levels of the exposure as related to the outcome (e.g., categories of exposure, or exposure measured as continuous variable)? | NA* | Yes | NA* | Yes | Yes | Yes | NA* | Yes | Yes |
9. Were the exposure measures (independent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
10. Was the exposure(s) assessed more than once over time? | NA* | Yes | NA* | NA* | NA* | NA* | NA* | NA* | NA* |
11. Were the outcome measures (dependent variables) clearly defined, valid, reliable, and implemented consistently across all study participants? | Yes | Yes | No | Yes | Yes | Yes | Yes | Yes | Yes |
12. Were the outcome assessors blinded to the exposure status of participants? | No | NR* | No | No | No | No | No | NR* | No |
13. Was loss to follow-up after baseline 20% or less? | NA* | No | NA* | NA* | NA* | NA* | NA* | NA* | NA* |
14. Were key potential confounding variables measured and adjusted statistically for their impact on the relationship between exposure(s) and outcome(s)? | No | Yes | Yes | Yes | Yes | Yes | Yes | Yes | Yes |
Overall evaluation | Fair | Good | Good | Good | Good | Good | Good | Good | Good |
PRISMA Checklist.
Title | 1 | Identify the report as a systematic review, meta-analysis, or both. | Title |
Structured summary | 2 | Provide a structured summary including, as applicable: background; objectives; data sources; study eligibility criteria, participants, and interventions; study appraisal and synthesis methods; results; limitations; conclusions and implications of key findings; systematic review registration number. | Abstract |
Rationale | 3 | Describe the rationale for the review in the context of what is already known. | Introduction |
Objectives | 4 | Provide an explicit statement of questions being addressed with reference to participants, interventions, comparisons, outcomes, and study design (PICOS). | Introduction |
Protocol and registration | 5 | Indicate if a review protocol exists, if and where it can be accessed (e.g., Web address), and, if available, provide registration information including registration number. | Methods |
Eligibility criteria | 6 | Specify study characteristics (e.g., PICOS, length of follow-up) and report characteristics (e.g., years considered, language, publication status) used as criteria for eligibility, giving rationale. | Methods |
Information sources | 7 | Describe all information sources (e.g., databases with dates of coverage, contact with study authors to identify additional studies) in the search and date last searched. | Methods |
Search | 8 | Present full electronic search strategy for at least one database, including any limits used, such that it could be repeated. | Methods |
Study selection | 9 | State the process for selecting studies (i.e., screening, eligibility, included in systematic review, and, if applicable, included in the meta-analysis). | Methods |
Data collection process | 10 | Describe method of data extraction from reports (e.g., piloted forms, independently, in duplicate) and any processes for obtaining and confirming data from investigators. | Methods |
Data items | 11 | List and define all variables for which data were sought (e.g., PICOS, funding sources) and any assumptions and simplifications made. | Methods |
Risk of bias in individual studies | 12 | Describe methods used for assessing risk of bias of individual studies (including specification of whether this was done at the study or outcome level), and how this information is to be used in any data synthesis. | Methods |
Summary measures | 13 | State the principal summary measures (e.g., risk ratio, difference in means). | – |
Synthesis of results | 14 | Describe the methods of handling data and combining results of studies, if done, including measures of consistency (e.g., I2) for each meta-analysis. | – |
Risk of bias across studies | 15 | Specify any assessment of risk of bias that may affect the cumulative evidence (e.g., publication bias, selective reporting within studies). | – |
Additional analyses | 16 | Describe methods of additional analyses (e.g., sensitivity or subgroup analyses, meta-regression), if done, indicating which were pre-specified. | – |
Study selection | 17 | Give numbers of studies screened, assessed for eligibility, and included in the review, with reasons for exclusions at each stage, ideally with a flow diagram. | Results |
Study characteristics | 18 | For each study, present characteristics for which data were extracted (e.g., study size, PICOS, follow-up period) and provide the citations. | Results |
Risk of bias within studies | 19 | Present data on risk of bias of each study and, if available, any outcome level assessment (see item 12). | Results |
Results of individual studies | 20 | For all outcomes considered (benefits or harms), present, for each study: (a) simple summary data for each intervention group (b) effect estimates and confidence intervals, ideally with a forest plot. | Supplementary material |
Synthesis of results | 21 | Present results of each meta-analysis done, including confidence intervals and measures of consistency. | Results |
Risk of bias across studies | 22 | Present results of any assessment of risk of bias across studies (see Item 15). | Results |
Additional analysis | 23 | Give results of additional analyses, if done [e.g., sensitivity or subgroup analyses, meta-regression (see Item 16)]. | – |
Summary of evidence | 24 | Summarize the main findings including the strength of evidence for each main outcome; consider their relevance to key groups (e.g., healthcare providers, users, and policy makers). | Discussion |
Limitations | 25 | Discuss limitations at study and outcome level (e.g., risk of bias), and at review-level (e.g., incomplete retrieval of identified research, reporting bias). | Discussion |
Conclusions | 26 | Provide a general interpretation of the results in the context of other evidence, and implications for future research. | Discussion |
Funding | 27 | Describe sources of funding for the systematic review and other support (e.g., supply of data); role of funders for the systematic review. | Funding |
PubMed Search Strategy.
PubMed | 16 October 2019 | (debt OR indebtedness OR over-indebtedness OR credit OR loan OR “financial problems” OR bankrupt) AND Asia AND (“mental disorder” OR “mental health” OR “mental illness” OR depression OR anxiety OR stress OR suicide OR “suicide ideation”) | Years: 2015–2019 | 56 | 48 | 8 |
See
PRISMA flow chart.
The identification search process was conducted from October 9, 2019 to October 10, 2019 for the Medline, PubMed, Scopus, and Web of Science database. ScienceDirect was searched on October 25, 2019. Using the search terms, a total of 462 articles were found from all five databases after excluding duplicates. During the first screening process, the title and abstract of each manuscript were screened for relevance and 431 titles were omitted.
Studies that were deemed acceptable were then screened for eligibility via the full text for their methodology and their findings. From the reading of the full text, several manuscripts were excluded with various reasons. These included manuscripts that discussed the effects of debt on other mental illnesses such as gambling addiction and post-traumatic embitterment, manuscripts that relate debt as a consequence of mental illnesses, manuscripts that mention a relationship between debt and depression, anxiety, suicide, or suicide ideation via citing other sources or presenting sample characteristics alone, manuscripts with participants below the age of 18, manuscripts that do not define the term financial problems or crisis, manuscripts that are review manuscripts as these are secondary sources, and manuscripts that combine debt with other financial problems to relate with mental disorders.
The number of manuscripts included and deemed suitable for review was nine. See
A total of nine studies were selected for review. All studies (100%) utilized a qualitative design, with only 11.11% using longitudinal data while the remaining 88.88% used cross-sectional data.
Overall, the most studied relationship is between debt and depression, with six out of nine manuscripts measuring depression as part of their study, accounting for 66.67% of the total manuscripts (Kaufman,
Four out of nine or 44.44% explored the relationship between debt and depression alone. One study or 11.11% of the found manuscripts examined the relationship between debt depression, anger, and sadness. The found manuscripts reported that those in debt experience depression, and there is evidence that those in debt experience greater debt than those without.
Only one study, or 11.11% of the found manuscripts explored the relationship between debt and depression and anxiety. Seponski et al. (
Two manuscripts or 22.22% of the manuscripts studied the relationship between debt and suicide ideations and behavior. Manning et al. (
Lastly, only one manuscript or 11.11% of the nine manuscripts discussed the relationship between debt and stress. The participants of this study are highly specific and limited to caregivers of inpatients in a hospital, and the manuscript's findings found that being in debt was statistically significant with caregiver stress.
In addition, this review examines the characteristics and culture background of participants reported by the studies.
Besides that, this present review identifies types of debts, definition of debts, and methods of measuring debt in order to understand the variability in defining and measuring debts as indicated in
Risk of bias of each individual study is determined using the National Heart, Lung, and Blood Institute (
The information from this risk of bias assessment aims to evaluate the quality of the research manuscripts included in the present review; regardless of the manuscript's evaluation, its strengths and weaknesses are used to generate methods to enhance future studies and to inform on the current state of research on the psychological effects of debt.
From the nine manuscripts, a noticeable trend is seen in the reporting of sample size. Only 25% of the found studies reported how the sample size was justified while others did not; this calls into question whether the sample used is reflective of the population as only 22.22% of the studies reported the participation rate of eligible participants.
The use of cross-sectional designs also results in an increase in bias due to the nature of the design itself. One of the limitations that is seen across all the cross-sectional studies is that causation cannot be established. This is seen in 77.78% of the total found manuscripts. Due to the nature of cross-sectional analyses, these studies do not allow enough time for an independent variable to have an effect or to occur or to be observed (National Heart, Lung, and Blood Institute,
A strength of the found manuscripts is that 88.88% of the found manuscripts used reliable, valid, or objective means of measure for their independent or dependent variable in line with the objectives of their study. However, this is not reflective of how specifically debt is measured in these studies. A few manuscripts define debt clearly and use self-reports or measure debt as a dichotomous variable, which may not accurately reflect the experience of debt. The most used tool is the Patient Health Questionnaire-9 (PHQ-9) in the study of depression; the PHQ-9 has been shown to have good internal consistency and acceptable inter-item correlations, and strong convergent validity with the PHQ-2 (Maroufizadeh et al.,
See
This review finds that few researches on the topic of debt and depression, anxiety, stress, or suicide were carried out in Asian countries. All the reviewed manuscripts use a quantitative design and a majority use cross-sectional data. The relationship between debt and depression is studied most frequently. The majority of the manuscripts did not define debt in their manuscripts. From these findings, a few points need to be addressed in terms of the methodology and findings of these manuscripts.
First, there is a need for more research into how debt impacts the mental health of the Asian population as there are very few recent manuscripts that explore this current topic. Simultaneously, focus on the studies of the psychological impact of debt should extend beyond depression, as the current manuscript finds a majority of the studies with Asian population mostly relate debt to depression (Kaufman,
Second, there is a need for more research into the roles of culture in understanding the relationship between debt and mental health. In the present review, there was lack of evidence on understanding the cultural explanation on the relationship between debts and mental health. This review finds that more research is needed in understanding of how cultural background such as socioeconomic factors impact the psychological effects of debt among Asians. The current manuscripts show some support that being in lower socioeconomic status increases the risk of depression due to debt (Lee et al.,
Although culture might play a role, there are limited information regarding this, and this might limit the interpretation on the role of culture in explaining the relationship between debts and mental health. In general, it might be difficult to attribute the findings to a specific ethnicity. Although there is an intention to explore the cultural explanations on the relationship between debts and loans, this is limited due to the limited cultural information provided in these reviewed manuscripts. From the current manuscripts, none specifically address how their specific culture encourages or discourages debt and explains the relationship between mental health and debt. This is important as indebtedness and the type of debt are found to be related to individuals who hold the value that money leads to prestige and power (Henchoz et al.,
It is argued that materialistic values in some Asian cultures hold more strongly compared to the West. For example, one study finds Singaporean women put greater emphasis on partner status and greater materialism-related happiness compared to American women (Li et al.,
Third, in terms of methodology, the use of quantitative methods in these studies incurs several strengths and limitations. Quantitative research is defined by how things are measured or counted, the distribution of the subject matter, how large an object is, how many of the thing is available, and how likely it is to meet the object that is discussed (Lune and Berg,
Fourth, it is observed that few manuscripts report types of debts. Sweet et al. (
In terms of results among the studies that relate debt to depression, the found research supports that individuals in debt experience greater depression (Kaufman,
In addition, only one manuscript found that self-reported anxiety is higher among those in debt (Seponski et al.,
In the study of stress and debt, high levels of stress were significantly related to caregiver's debt burden (Sharma et al.,
Two manuscripts found higher suicide ideation among females in debt (Manning et al.,
From manuscripts included in this review, the main findings revolve around establishing a relationship between debt and the mental health issue studied with only one study exploring how income levels affect the relationship between debt and depression (Lee et al.,
Although a conclusion about the state of debt and its effects in Asia cannot be drawn from these few studies, the trend that is observed from these findings is that among these Asian participants, there is evidence that being in debt is positively related to depression, anxiety, stress, and suicide ideation. The study on the effects of debt also needs to be made a primary objective as majority of these manuscripts do not look into debt as a primary factor of mental illness. This calls for research with more precise methodology especially in defining and measuring debt. In addition, other factors that influence the relationship between debt and mental illness need to be explored.
The findings of this study are restricted by several limitations. First, the choice to omit unpublished literature may incur some bias on the findings of this study. The use of highly specific participants studied in these research manuscripts also limits the generalizability of these findings.
Overall, the present review finds that there is lack of research on the effects of debt on mental health issues such as depression, anxiety, stress, and suicide. Methodologically, there is a need to understand the context behind the relationship between debt and mental health issues and clearer definitions of debt.
NA, ET, NI, and NC contributed to conception and design of the study. NA and ET organized the databases and wrote the first draft of the manuscript NA, ET, and NI performed the statistical analysis. MM, AZ, RI, TT, ET, and NA revised the manuscript. All authors contributed to manuscript revision, and read and approved the submitted version.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.