Financial Stressors and Suicide

Eric B Elbogen; Tess A Gilbert; Bryann B DeBeer; Elizabeth R Hooker; Sarah Shull; Nathan A Kimbrel; Terri K Pogoda; Jason I Chen; Kathleen F Carlson

doi:10.1001/jamanetworkopen.2026.20981

. 2026 Jul 27;9(7):e2620981. doi: 10.1001/jamanetworkopen.2026.20981

Financial Stressors and Suicide

Eric B Elbogen ^1,^2,^3,^✉, Tess A Gilbert ⁴, Bryann B DeBeer ^1,⁵, Elizabeth R Hooker ^4,⁶, Sarah Shull ⁴, Nathan A Kimbrel ^3,⁷, Terri K Pogoda ^8,⁹, Jason I Chen ^4,¹⁰, Kathleen F Carlson ^4,⁶

¹Department of Veterans Affairs, Rocky Mountain Mental Illness Research, Education, and Clinical Center (MIRECC) for Suicide Prevention, Aurora, Colorado

²Department of Veterans Affairs, National Veterans Financial Resource Center, Aurora, Colorado

³Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Durham, North Carolina

⁴Department of Veterans Affairs, VA Portland Health Care System, Center to Improve Veteran Involvement in Care, Portland, Oregon

⁵Department of Physical Medicine and Rehabilitation, University of Colorado Anschutz Medical Campus, Aurora, Colorado

⁶School of Public Health, Oregon Health & Sciences University, Portland

⁷Department of Veterans Affairs, Mid-Atlantic MIRECC, Durham, North Carolina

⁸Department of Veterans Affairs, Center for Health Optimization and Implementation Research, Boston, Massachusetts

⁹Department of Health Law, Policy & Management, Boston University School of Public Health, Boston, Massachusetts

¹⁰Department of Psychiatry, Oregon Health & Sciences University, Portland

Accepted for Publication: April 26, 2026.

Published: July 27, 2026. doi:10.1001/jamanetworkopen.2026.20981

Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License, which does not permit alteration or commercial use, including those for text and data mining, AI training, and similar technologies. © 2026 Elbogen EB et al. JAMA Network Open.

^✉

Corresponding Author: Eric B. Elbogen, PhD, Department of Psychiatry and Behavioral Sciences, Duke University School of Medicine, Duke North Pavilion Box 102506, Durham, NC 27710 (eric.elbogen@duke.edu).

Author Contributions: Dr Carlson and Ms Gilbert had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Elbogen, Gilbert, DeBeer, Hooker, Kimbrel, Carlson.

Acquisition, analysis, or interpretation of data: Elbogen, Gilbert, DeBeer, Hooker, Shull, Pogoda, Chen, Carlson.

Drafting of the manuscript: Elbogen, Gilbert, Kimbrel, Carlson.

Critical review of the manuscript for important intellectual content: Elbogen, DeBeer, Hooker, Shull, Kimbrel, Pogoda, Chen, Carlson.

Statistical analysis: Gilbert, Hooker, Carlson.

Obtained funding: Elbogen, DeBeer, Kimbrel, Carlson.

Administrative, technical, or material support: Elbogen, Gilbert, DeBeer, Chen, Carlson.

Supervision: Elbogen.

Conflict of Interest Disclosures: Dr DeBeer reported receiving grants from Face the Fight during the conduct of the study as well as grants from the US Department of Veterans Affairs (VA) and Fund for a Safer Future outside the submitted work. Dr Carlson reported receiving grants from the VA during the conduct of the study. No other disclosures were reported.

Funding/Support: The research reported in this publication was supported by the VA (SPRINT COR#19-490) and the Face the Fight. Dr Kimbrel was supported by a VA Research Career Scientist Award (#I01BX005881) from the VA Office of Research and Development.

Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Disclaimer: The content is solely the responsibility of the authors and does not necessarily reflect the position or policy of the VA, the US government, or any other affiliated institution.

Data Sharing Statement: See Supplement 2.

^✉

Corresponding author.

PMCID: PMC13409012 PMID: 42507447

Key Points

Question

Is suicide associated with financial stressors in the previous year, as verified by national credit data?

Findings

This case-control study of 17 439 veterans found that delinquent mortgage payments, repossessions, late credit card payments, and lower credit scores were associated with higher odds of suicide. Cumulative financial stressors and declining credit scores had the most robust associations with suicide, persisting in multivariable analyses adjusting for demographics and, among US Department of Veterans Affairs health care users, marital status and prior psychiatric diagnoses.

Meaning

Financial stressors were more prevalent in the previous 12 months among cases compared with controls, suggesting a need to address financial well-being as a component of suicide prevention.

This case-control study examines whether financial stressors in the year before death are associated with suicide risk among US veterans.

Abstract

Importance

Financial strain is associated with suicide risk; however, no research has examined financial stressors in the year preceding suicide using national credit data.

Objective

To assess financial stressors in the 12 months preceding suicide in a national sample of veterans by linking financial, mortality, and health care records.

Design, Setting, and Participants

This case-control study of 5000 veterans who died by suicide (January 1, 2015 to December 31, 2017) used mortality data from the US Department of Veterans Affairs (VA) and the US Department of Defense. Cases were matched to 12 500 living controls on age, sex, VA health care use, and suicide index date. Analyses were conducted in December 2025.

Exposures

Financial stressors in the prior 12 months were identified in TransUnion credit records provided by FinThrive.

Main Outcomes and Measures

The primary outcome was death by suicide. Differences between cases and controls were analyzed using multivariable conditional logistic regression, adjusting for demographics and, among VA users, marital status and diagnoses of depression, posttraumatic stress disorder, and substance use disorder.

Results

Among 17 439 veterans included in this study, the mean (SD) age was 52.9 (15.7) years, and 94.6% were male. Suicide was associated with having (vs not having) delinquent mortgage payments (odds ratio [OR], 1.4; 95% CI, 1.2-1.7), repossessions (OR, 1.9; 95% CI, 1.3-2.7), and credit card payments 30 or more days past due (OR, 1.7; 95% CI, 1.6-1.9). Lower credit score categories were associated with increasing odds of suicide. Veterans with 3 or more financial stressors had higher odds of suicide (OR, 2.4; 95% CI, 1.9-3.0) compared with those who had none. Among individuals with available data, credit score declines of 51 to 100 points (OR, 1.3; 95% CI, 1.1-1.5) and greater than 100 points (OR, 2.8; 95% CI, 2.3-3.5) over 1 year, relative to no change in score, were associated with increased odds of suicide. Cumulative financial stressors and declining credit scores had the highest ORs for suicide when adjusting for demographics and, among VA users, marital status and prior psychiatric diagnoses. ORs were lower among VA users than among non-VA users.

Conclusions and Relevance

In this case-control study, suicide was associated with financial stressors in the prior 12 months. Incorporating financial stressors into clinical evaluations and safety planning may aid in identifying patients at risk for suicide. Policymakers and researchers could consider strategies to detect and mitigate financial stressors as part of comprehensive suicide prevention efforts.

Introduction

Suicide rates have risen in the US during the past 2 decades.^1,2 Although depression and other psychiatric conditions are well-established risk factors,^3,4 emerging research highlights socioeconomic factors,^5,6,7,8 specifically financial stressors,^9,10,11 as contributors to suicide risk.¹² Suicide rates coincide with economic cycles,^13,14 rising during periods of financial downturn and declining when economic conditions improve.^5,12,15 Policy interventions, such as tax credits and nutrition assistance, correlate with reduced suicide rates.^16,17,18,19 Individuals facing financial crises (eg, unemployment) show increased risk.^10,20 Meta-analyses reveal adults with unsecured debt are more than 5 times as likely to attempt or complete suicide than those without.²¹ Machine learning models in a nationally representative longitudinal study identified financial strain, including debt and inability to pay bills, as a strong predictor of suicide attempts.¹⁰

Knowledge gaps remain about financial stressors and suicide. Most studies measure suicidal ideation or attempts^9,10,20,22 rather than death by suicide. Financial burden is usually measured broadly,^{7,11,20,23,24} leaving unclear which specific financial stressors affect risk. Little is known about financial stressors in the year before suicide because studies are often cross-sectional,^7,14,21,24 and there is limited research examining variables in concrete risk periods.²⁵ Clinical variables, such as depression and substance use disorder, are infrequently included in research on financial stressors and suicide.^9,20,26 No studies to date have linked objectively verified national credit data to official suicide and health care records.

This study examines associations between specific financial stressors and subsequent death by suicide through linked national health care, mortality, and credit data. Leveraging data from the US Department of Veterans Affairs (VA) presents a unique opportunity to analyze credit, mortality, and health care data comprehensively. Many veterans face financial stressors^27,28 correlated with higher suicide risk.^22,25 Although this study involves veterans, the insights gained may be broadly relevant for suicide prevention across populations. By identifying financial risk factors, this research aims to enhance targeted clinical assessments and patient safety planning, inform policy initiatives, and contribute to suicide prevention.

Methods

Study Design

We conducted a frequency-matched, case-control study to analyze associations between financial stressors and suicide. Data from the VA/Department of Defense Identity Repository (VADIR), the VA/Department of Defense Mortality Data Repository (MDR), and the VA Corporate Data Warehouse (CDW) were deterministically linked using veterans’ social security numbers to TransUnion credit records provided by FinThrive. Use of FinThrive data was permitted through an intra-agency data use agreement with the Veterans Health Administration Financial Services Center. The study received approval from the VA Portland Health Care System Institutional Review Board and Research and Development Committees. A waiver of informed consent was granted because this retrospective study relied on linkage of large administrative datasets spanning multiple years and involving participants who could not feasibly be re-contacted. The study posed minimal risk, included robust protections for confidentiality, and could not practicably be conducted without the waiver. We followed the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) and Reporting of Studies Conducted Using Observational Routinely Collected Health Data (RECORD) reporting guidelines.

Using administrative data from CDW and VADIR, we identified VA-enrolled and non–VA-enrolled veterans, respectively. These datasets were then linked to the MDR to identify deceased veterans and cause of death, a validated method for identifying veteran suicide.²⁹ Veterans were eligible to be included in the sample if they were alive at the study start date (January 1, 2015), were out of military service for at least 30 days before sampling, and were aged 18 to 105 years at the study end date (December 31, 2017). Veterans who died by suicide during this period (potential cases) were identified using International Classification of Diseases and Other Related Health Problems, Tenth Revision codes (X60-X84 and Y87.0) as documented in the MDR; potential controls were those who were alive during the same period. Data cleaning included standardizing formats, examining data structure, assessing missing data, and normalizing text fields.

The 3-year study period (January 1, 2015, to December 31, 2017) was divided into 12 quarters into which 5000 cases and 12 500 living controls were proportionally selected using a 1:2.5 frequency match. This sample size represented the maximum number of veterans for whom we could acquire data from FinThrive with available resources; the 1:2.5 ratio was used for statistical efficiency.³⁰ The number of cases selected per quarter was calculated using the proportion of suicide deaths that occurred in each quarter based on the total number of suicide deaths across the study period. To select a total of 5000 cases, the proportion of suicide deaths was multiplied by 5000 to indicate the number of cases to be randomly sampled, per quarter, into the study from the total number of suicide deaths that occurred during that quarter.

Controls were matched to cases based on age, sex, and VA health care enrollment and utilization levels during the 2 years before veterans’ index dates. Age was stratified into 7 categories (18-34, 35-44, 45-54, 55-64, 65-74, 75-84, and ≥85 years), whereas VA enrollment or use was stratified into 4 categories (non–VA-enrolled and VA-enrolled with 0 visits, 1-14 visits, or ≥15 visits during the 2 years before their index date). This stratification resulted in a total of 56 strata. We multiplied the resulting number within those strata by 2.5 to determine the number of controls sampled from among the controls within that quarter-strata. The same process was repeated for each stratum and quarter to result in a total of 12 500 selected controls. The final study sample had 17 500 veterans. The index date was the date of death for suicide decedents, whereas for controls, we used a randomly selected date within the matched quarter. Credit data were queried from FinThrive once per quarter between 2015 and 2017, and once annually (in quarter 4) in 2013 and 2014. A total of 17 439 veterans who matched to FinThrive data comprised the analytic sample (eFigure in Supplement 1).

Outcome and Exposures

The primary outcome was death by suicide, verified by the MDR. For exposures, FinThrive data were used to measure financial stressors in the year before the veterans’ index quarters. Stressors included having at least 1 delinquent mortgage payment, repossession of an asset (eg, vehicles), foreclosure on a property, Chapter 7 or 13 bankruptcy filing, and delinquent credit card payment (≥30 days past due). Credit ranges were categorized as excellent (781-850), good (661-780), fair (601-660), and poor (300-600) based on credit score data provided by FinThrive.³¹ A summary variable was calculated based on number of financial stressors experienced (0, 1-2, and ≥3) from each indicator listed above (including a poor credit score). One-year change in credit score was calculated by the difference between credit scores reported during the quarter immediately preceding the index quarter and at 1 year prior. Change in credit score was categorized as no change (defined as a positive or negative change of 0 to 9 points), positive change (defined as positive change of 10 points or more), or reductions of 10 to 25, 26 to 50, 51 to 100, and more than 100 points.

Age and sex were extracted from the CDW for VA-enrolled veterans and from VADIR for non–VA-enrolled veterans. Race and ethnicity was extracted for all veterans from VADIR with categories as described previously.^32,33 Additional data were available for users of VA health care, including marital status as well as prior depression, posttraumatic stress disorder (PTSD), and substance use disorder (SUD) diagnoses (including alcohol use disorder), identified through International Classification of Diseases, Tenth Revision, Ninth Revision, Clinical Modification and International Classification of Diseases and Other Related Health Problems, Tenth Revision, Clinical Modification codes (eTable in Supplement 1). Prior diagnoses were defined as 2 or more diagnosis codes assigned during VA outpatient encounters, or 1 or more code assigned during inpatient stays, at any time in the year before index quarters (ie, before the 1-year look-back period for financial stressors).

Statistical Analysis

Analyses were conducted in December 2025. Frequencies of demographic data and financial stressors were cross-tabulated by suicide decedents vs living controls. Among VA-using veterans (those with ≥1 visit in the 2 years before their index date), we additionally present frequencies for marital status and prior psychiatric diagnoses. To estimate associations between financial stressors and suicide, we conducted bivariable and multivariable conditional logistic regression, accounting for the matched design.

To help address any residual confounding, multivariable models were adjusted for age as a continuous variable and race and ethnicity. Odds ratios (OR) and 95% CIs are presented in tables. Data were missing for relatively small proportions of veterans for each financial stressor variable, ranging from 0.6% to 5.4%; participants with missing financial data were excluded from each respective model. Veterans with missing or unknown data for race and ethnicity (31.4%) or marital status (1.3%) were retained in analyses, using separate categories for “missing or unknown.”

To examine potential differences in patterns between veterans with vs without VA health care use, we also conducted bivariable and multivariable conditional logistic regression analyses in stratified models for each group. Minimally adjusted multivariable models controlled for age and race and ethnicity, similar to those for the full sample. Availability of CDW data for veterans who used VA services enabled additional adjustment for marital status and prior depression, PTSD, and SUD diagnoses; we refer to these models as fully adjusted.

Quarterly credit data queries were made between 2015 and 2017, with annual queries made in quarters 4 of 2013 and 2014. Further, credit scores were measured from the quarter before the index quarter. As a result, given the parameters of the credit data inquiry, credit scores from 1 year prior were not available for all veterans in the sample (eFigure in Supplement 1). One-year credit score change could only be computed for the subset of veterans (12 228 [70.1%]) with index dates in 2016 to 2017 or in quarter 1 of 2015.

For this subset with complete credit score data available in both the quarter before the index quarter and 1 year prior, we examined associations between 1-year change in credit scores and suicide; multivariable analyses controlled for variables as described. This subset with available 1-year change data was similar to the full sample across levels of each of the aforementioned covariates. All P values were 2-sided, and statistical significance was defined as P < .05. Data were analyzed using SAS, version 9.4 (SAS Institute Inc).

Results

Table 1 presents demographic characteristics of 17 439 veterans, among whom 94.6% were male and 5.4% were female, with a mean (SD) age of 52.9 (15.7) years. Among these veterans, 0.7% were American Indian or Alaska Native, 2.7% were Asian or Pacific Islander, 9.6% were Black, 3.3% were Hispanic, 51.6% were White, 0.7% were another race (including multiple races), and 31.4% had missing or unknown race; the specific racial groups defined as another race were not available. Most veterans in the sample (51.4%) were not enrolled in VA health care. Among 8484 veterans enrolled in VA health care, 3307 (39.0%) had 0 visits in the 2 years before their index date, 2248 (26.5%) had 1 to 14 visits, and 2929 (34.5%) had 15 or more visits.

Table 1. Characteristics of National Sample of Suicide Decedents and Matched Living Controls.

Characteristic	No. (%)
Characteristic	Total sample (N = 17 439)	Suicide decedents (n = 4993)	Living controls (n = 12 446)
Age, mean (SD)	52.9 (15.7)	52.7 (15.7)	53.0 (15.6)
Age group, y
18-34	2818 (16.2)	807 (16.1)	2011 (16.1)
35-44	2549 (14.6)	728 (14.6)	1821 (14.6)
45-54	4247 (24.4)	1214 (24.3)	3033 (24.4)
55-64	3998 (23.0)	1148 (23.0)	2850 (22.9)
65-74	2395 (13.7)	686 (13.7)	1709 (13.7)
75-84	926 (5.3)	264 (5.3)	662 (5.3)
≥85	506 (2.9)	146 (2.9)	360 (2.9)
Sex
Male	16 491 (94.6)	4723 (94.6)	11 768 (94.6)
Female	948 (5.4)	270 (5.4)	678 (5.5)
Race and ethnicity
American Indian or Alaska Native	116 (0.7)	42 (0.8)	74 (0.6)
Asian or Pacific Islander	470 (2.7)	123 (2.5)	347 (2.8)
Black	1674 (9.6)	211 (4.2)	1463 (11.8)
Hispanic^a	582 (3.3)	122 (2.4)	460 (3.7)
White	8995 (51.6)	2765 (55.4)	6230 (50.1)
Other^b	125 (0.7)	30 (0.6)	95 (0.8)
Missing or unknown	5477 (31.4)	1700 (34.1)	3777 (30.4)
VA health care use or enrollment^c
No VA use (not VA enrolled)	8955 (51.4)	2568 (51.4)	6387 (51.3)
No VA use (VA enrolled, 0 visits)	3307 (19.0)	945 (18.9)	2362 (19.0)
VA use, 1-14 visits (enrolled)	2248 (12.9)	641 (12.8)	1607 (12.9)
VA use, ≥15 visits (enrolled)	2929 (16.8)	839 (16.8)	2090 (16.8)
Marital status^d
Married	2373 (45.8)	546 (36.9)	1827 (49.4)
Not married	2736 (52.9)	911 (61.6)	1825 (49.4)
Missing or unknown	68 (1.3)	23 (1.6)	45 (1.2)
Depression diagnoses^d
Yes	1914 (37.0)	701 (47.4)	1213 (32.8)
No	3263 (63.0)	779 (52.6)	2484 (67.2)
PTSD diagnoses^d
Yes	1154 (22.3)	384 (26.0)	770 (20.8)
No	4023 (77.7)	1096 (74.1)	2927 (79.2)
SUD diagnoses^d
Yes	1291 (24.9)	520 (35.1)	771 (20.9)
No	3886 (75.1)	960 (64.9)	2926 (79.1)

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Abbreviations: CDW, Veterans Affairs Corporate Data Warehouse; PTSD, posttraumatic stress disorder; SUD, substance use disorder; VA, US Department of Veterans Affairs; VADIR, VA/Department of Defense Identity Repository.

^{^a}

VADIR Hispanic category defined as follows: “Regardless of race; this is ethnicity grouped into a race code,” according to the VADIR Data Dictionary (updated April 2, 2019).

^{^b}

VADIR Other category includes those with 2 or more races; the specific racial groups defined as another race were not available.

^{^c}

Number of visits found in CDW in 2 years before index quarter start date.

^{^d}

Includes only veterans who use Veterans Affairs health care services (n = 5177).

Associations between financial distress variables and suicide are shown in Table 2. In multivariable analyses, increased odds of suicide were observed among veterans with, vs without, delinquent mortgage payments (OR, 1.4; 95% CI, 1.2-1.7), repossessions (OR, 1.9; 95% CI, 1.3-2.7), and credit card payments 30 or more days past due (OR, 1.7; 95% CI, 1.6-1.9); these associations were also present in bivariable analyses. Compared with veterans with excellent credit scores, those with good (OR, 1.3; 95% CI, 1.2-1.5), fair (OR, 1.6; 95% CI, 1.5-1.8), or poor (OR, 2.2; 95% CI, 2.0-2.5) credit scores had higher odds of suicide, with ORs suggesting a dose-response pattern with worsening categories. Veterans with 1 to 2 financial stressors (OR, 1.7; 95% CI, 1.6-1.8) and those with 3 or more stressors (OR, 2.4; 95% CI, 1.9-3.0), had increasing odds of suicide compared with those who had none.

Table 2. Associations Between Financial Stressors and Subsequent Death by Suicide (N = 17 439).

Financial stressor	No. (%)		OR (95% CI)
Financial stressor	Suicide decedents (n = 4993	Living controls (n = 12 446	Bivariable regression models	Multivariable regression models^a
Delinquent mortgage payment^b
Yes	271 (5.6)	500 (4.1)	1.4 (1.2-1.6)^c	1.4 (1.2-1.7)^c
No	4561 (94.4)	11 668 (95.9)	1 [Reference]	1 [Reference]
Repossessions^b
Yes	58 (1.2)	82 (0.7)	1.8 (1.3-2.5)^c	1.9 (1.3-2.7)^c
No	4774 (98.8)	12 086 (99.3)	1 [Reference]	1 [Reference]
Foreclosures^b
Yes	19 (0.4)	35 (0.3)	1.4 (0.8-2.4)	1.2 (0.7-2.2)
No	4813 (99.6)	12 133 (99.7)	1 [Reference]	1 [Reference]
Bankruptcies^d
Yes	42 (0.9)	78 (0.6)	1.1 (1.0-1.3)	1.1 (1.0-1.3)
No	4923 (99.1)	12 290 (99.4)	1 [Reference]	1 [Reference]
Credit card payments ≥30 d past due^b
Yes	723 (15.0)	1176 (9.7)	1.7 (1.5-1.8)^c	1.7 (1.6-1.9)^c
No	4109 (85.0)	10 992 (90.3)	1 [Reference]	1 [Reference]
Credit score^e
Excellent credit: 781-850	941 (20.0)	3231 (27.2)	1 [Reference]	1 [Reference]
Good credit: 661-780	1234 (26.2)	3441 (28.9)	1.3 (1.2-1.5)^c	1.3 (1.2-1.5)^c
Fair credit: 601-660	761 (16.1)	1828 (15.4)	1.6 (1.4-1.8)^c	1.6 (1.5-1.8)^c
Poor credit: 300-600	1778 (37.7)	3394 (28.5)	2.1 (1.9-2.3)^c	2.2 (2.0-2.5)^c
No. of financial stressors^f
0	2666 (56.9)	7913 (67.0)	1 [Reference]	1 [Reference]
1 or 2	1873 (40.0)	3700 (31.3)	1.6 (1.5-1.7)^c	1.7 (1.6-1.8)^c
≥3	149 (3.2)	204 (1.7)	2.2 (1.8-2.8)^c	2.4 (1.9-3.0)^c

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Abbreviation: OR, odds ratio.

^{^a}

Multivariable models controlled for age and race and ethnicity.

^{^b}

n = 439 (2.5%) missing.

^{^c}

Indicates statistical significance.

^{^d}

Chapter 7 or 13 bankruptcy; n = 106 (0.6%) missing.

^{^e}

n = 831 (4.8%) missing.

^{^f}

n = 934 (5.4%) missing.

Table 3 and Table 4 present associations between financial stressors and suicide among veterans with and without VA health care use. Among veterans who used VA services, in the minimally adjusted multivariable models, we observed statistically significant associations between suicide and credit card payments 30 or more days past due (OR, 1.5; 95% CI, 1.2-1.8); good (OR, 1.3; 95% CI, 1.0-1.5), fair (OR, 1.4; 95% CI, 1.1-1.7), or poor (OR, 1.6; 95% CI, 1.4-2.0) credit scores compared with excellent scores; and 1 to 2 financial stressors (OR, 1.4; 95% CI, 1.2-1.6) and 3 or more financial stressors compared with none (OR, 1.7; 95% CI, 1.2-2.5); these associations were all also present in the bivariable models. In fully adjusted models that also controlled for marital status and prior depression, PTSD, and SUD diagnoses, associations remained for credit card payments 30 or more days past due (OR, 1.5; 95% CI, 1.2-1.8) and 3 or more financial stressors (OR, 1.7; 95% CI, 1.1-2.5) but were no longer present for any credit score categories. Among veterans who did not use VA services, nearly all financial distress indicators had statistically significant associations with suicide in both bivariable and multivariable models. Comparing the 2 subgroups, OR patterns suggested greater and more consistent odds of suicide with most financial distress indicators among non-VA users compared with VA users.

Table 3. Associations Between Financial Stressors and Subsequent Death by Suicide Among Veterans With Veterans Affairs Health Care Use (N = 5177).

Financial stressor	No. (%)		OR (95% CI)
Financial stressor	Suicide decedents (n = 1480)	Living controls (n = 3697)	Bivariable regression models	Multivariable regression models (minimally adjusted)^a	Multivariable regression models (fully adjusted)^b
Delinquent mortgage payment^c
Yes	70 (4.9)	160 (4.4)	1.1 (0.8-1.5)	1.1 (0.8-1.5)	1.2 (0.9-1.7)
No	1367 (95.1)	3481 (95.6)	1 [Reference]	1 [Reference]	1 [Reference]
Repossessions^c
Yes	20 (1.4)	38 (1.0)	1.3 (0.8-2.3)	1.3 (0.7-2.3)	1.2 (0.7-2.2)
No	1417 (98.6)	3603 (99.0)	1 [Reference]	1 [Reference]	1 [Reference]
Foreclosures^c
Yes	9 (0.6)	18 (0.5)	1.3 (0.6-2.9)	1.1 (0.5-2.5)	1.1 (0.5-2.4)
No	1428 (99.4)	3623 (99.5)	1 [Reference]	1 [Reference]	1 [Reference]
Bankruptcies^d
Yes	12 (0.8)	34 (0.9)	0.9 (0.5-1.7)	1.0 (0.5-2.0)	1.3 (0.6-2.5)
No	1462 (99.2)	3641 (99.1)	1 [Reference]	1 [Reference]	1 [Reference]
Credit card payments ≥30 d past due^c
Yes	222 (15.5)	428 (11.8)	1.4 (1.2-1.6)^e	1.5 (1.2-1.8)^e	1.5 (1.2-1.8)^e
No	1215 (84.6)	3213 (88.2)	1 [Reference]	1 [Reference]	1 [Reference]
Credit score^f
Excellent credit: 781-850	251 (18.0)	787 (22.2)	1 [Reference]	1 [Reference]	1 [Reference]
Good credit: 661-780	394 (28.2)	1012 (28.5)	1.3 (1.0-1.5)^e	1.3 (1.0-1.5)^e	1.1 (0.9-1.4)
Fair credit: 601-660	241 (17.3)	601 (16.9)	1.3 (1.1-1.6)^e	1.4 (1.1-1.7)^e	1.1 (0.9-1.4)
Poor credit: 300-600	511 (36.6)	1148 (32.4)	1.5 (1.2-1.8)^e	1.6 (1.4-2.0)^e	1.2 (1.0-1.5)
No. of financial stressors^g
0	811 (58.2)	2228 (63.2)	1 [Reference]	1 [Reference]	1 [Reference]
1 or 2	534 (38.3)	1212 (34.4)	1.2 (1.1-1.4)^e	1.4 (1.2-1.6)^e	1.1 (1.0-1.3)
≥3	48 (3.5)	86 (2.4)	1.6 (1.1-2.5)^e	1.7 (1.2-2.5)^e	1.7 (1.1-2.5)^e

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Abbreviation: OR, odds ratio.

^{^a}

Minimally adjusted multivariable models controlled for age and race and ethnicity.

^{^b}

Fully adjusted multivariable models controlled for age, race and ethnicity, marital status, and prior depression, posttraumatic stress disorder, and substance use disorder diagnoses.

^{^c}

n = 99 (1.9%) missing.

^{^d}

Chapter 7 or 13 bankruptcy; n = 28 (0.5%) missing.

^{^e}

Indicates statistical significance.

^{^f}

n = 232 (4.5%) missing.

^{^g}

n = 258 (5.0%) missing.

Table 4. Associations Between Financial Stressors and Subsequent Death by Suicide Among Veterans With No Veterans Affairs Health Care Use (N = 12 262).

Financial stressor	No. (%)		OR (95% CI)
Financial stressor	Suicide decedents (n = 3513)	Living controls (n = 8749)	Bivariable regression models	Multivariable regression models^a
Delinquent mortgage payment^b
Yes	201 (5.9)	340 (4.0)	1.5 (1.3-1.8)^c	1.6 (1.3-1.9)^c
No	3194 (94.1)	8187 (96.0)	1 [Reference]	1 [Reference]
Repossessions^b
Yes	38 (1.1)	44 (0.5)	2.2 (1.4-3.4)^c	2.5 (1.6-3.9)^c
No	3357 (98.9)	8483 (99.5)	1 [Reference]	1 [Reference]
Foreclosures^b
Yes	10 (0.3)	17 (0.2)	1.5 (0.7-3.2)	1.4 (0.6-3.0)
No	3385 (99.7)	8510 (99.8)	1 [Reference]	1 [Reference]
Bankruptcies^d
Yes	30 (0.9)	44 (0.5)	1.7 (1.1-2.7)^c	1.6 (1.0-2.6)^c
No	3461 (99.1)	8649 (99.5)	1 [Reference]	1 [Reference]
Credit card payments ≥30 d past due^b
Yes	501 (14.8)	748 (8.8)	1.8 (1.6-2.0)^c	1.9 (1.6-2.1)^c
No	2894 (85.2)	7779 (91.2)	1 [Reference]	1 [Reference]
Credit score^e
Excellent credit: 781-850	690 (20.8)	2444 (29.3)	1 [Reference]	1 [Reference]
Good credit: 661-780	840 (25.3)	2429 (29.1)	1.3 (1.2-1.5)^c	1.3 (1.2-1.5)^c
Fair credit: 601-660	520 (15.7)	1227 (14.7)	1.7 (1.5-1.9)^c	1.8 (1.5-2.0)^c
Poor credit: 300-600	1267 (38.2)	2246 (26.9)	2.3 (2.1-2.6)^c	2.5 (2.3-2.9)^c
No. of financial stressors^f
0	1855 (56.3)	5685 (68.6)	1 [Reference]	1 [Reference]
1 or 2	1339 (40.6)	2488 (30.0)	1.7 (1.6-1.9)^c	1.9 (1.7-2.0)^c
≥3	101 (3.1)	118 (1.4)	2.7 (2.1-3.6)^c	2.8 (2.1-3.7)^c

Open in a new tab

Abbreviation: OR, odds ratio.

^{^a}

Multivariable models controlled for age and race and ethnicity.

^{^b}

n = 340 (2.8%) missing.

^{^c}

Indicates statistical significance.

^{^d}

Chapter 7 or 13 bankruptcy; n = 78 (0.6%) missing.

^{^e}

n = 599 (4.9%) missing.

^{^f}

n = 676 (5.5%) missing.

Veterans with 1-year negative changes in credit scores of 51 to 100 points (OR, 1.3; 95% CI, 1.1-1.5) or greater than 100 points (OR, 2.8; 95% CI, 2.3-3.5) had higher odds of suicide compared with those with no change in credit score in multivariable analyses, as well as in bivariable analyses (Table 5). We found similar results for non-VA users. For VA users, changes in credit score in a positive direction (OR, 0.8; 95% CI, 0.6-0.9) and 10- to 25-point reductions (OR, 0.7; 95% CI, 0.6-1.0), were associated with reduced odds of suicide relative to those with no change; these associations remained significant in the fully adjusted model. Veterans with greater than 100-point reductions had increased odds of suicide, compared with those who had no change, in the multivariable models (OR, 2.3; 95% CI, 1.5-3.4) as well as bivariable models.

Table 5. Associations Between 1-Year Change in Credit Score and Subsequent Death by Suicide^a.

1-y Change in credit score	No. (%)		OR (95% CI)
1-y Change in credit score	Suicide decedents	Living controls	Bivariable regression models	Multivariable regression models (minimally adjusted)^b	Multivariable regression models (fully adjusted)^c
All veterans (n = 12 228)
No change^d	1029 (29.7)	2688 (30.7)	1 [Reference]	1 [Reference]	NA
Positive change^e	1209 (34.9)	3428 (39.1)	0.9 (0.8-1.0)	0.9 (0.8-1.0)
10- to 25-Point reduction^f	388 (11.2)	1000 (11.4)	1.0 (0.9-1.2)	1.0 (0.9-1.2)
26- to 50-Point reduction^f	346 (10.0)	838 (9.6)	1.1 (0.9-1.2)	1.1 (0.9-1.3)
51- to 100-Point reduction^f	294 (8.5)	607 (6.9)	1.3 (1.1-1.5)^g	1.3 (1.1-1.5)^g
>100-Point reduction^f	203 (5.9)	198 (2.3)	2.7 (2.2-3.3)^g	2.8 (2.3-3.5)^g
Veterans with VA use (n = 3645)
No change^d	323 (31.4)	749 (28.6)	1 [Reference]	1 [Reference]	1 [Reference]
Positive change^e	342 (33.2)	1031 (39.4)	0.8 (0.6-0.9)^g	0.8 (0.6-0.9)^g	0.8 (0.6-0.9)^g
10- to 25-Point reduction^f	97 (9.4)	301 (11.5)	0.7 (0.6-1.0)^g	0.7 (0.6-1.0)^g	0.7 (0.5-0.9)^g
26- to 50-Point reduction^f	113 (11.0)	261 (10.0)	1.0 (0.8-1.3)	1.0 (0.8-1.3)	1.0 (0.8-1.3)
51- to 100-Point reduction^f	95 (9.2)	215 (8.2)	1.0 (0.8-1.3)	1.0 (0.8-1.4)	1.0 (0.8-1.4)
>100-Point reduction^f	59 (5.7)	59 (2.3)	2.3 (1.5-3.4)^g	2.3 (1.5-3.4)^g	2.3 (1.6-3.5)^g
Veterans with no VA use (n = 8583)
No change^d	709 (28.9)	1939 (31.6)	1 [Reference]	1 [Reference]	NA
Positive change^e	867 (35.5)	2397 (39.0)	1.0 (0.9-1.1)	1.0 (0.9-1.1)
10- to 25-Point reduction^f	291 (11.9)	699 (11.4)	1.1 (1.0-1.3)	1.1 (1.0-1.3)
26- to 50-Point reduction^f	233 (9.6)	577 (9.4)	1.1 (0.9-1.3)	1.1 (0.9-1.3)
51- to 100-Point reduction^f	199 (8.2)	392 (6.4)	1.4 (1.2-1.7)^g	1.4 (1.2-1.7)^g
>100-Point reduction^f	144 (5.9)	139 (2.3)	2.8 (2.2-3.6)^g	3.0 (2.3-3.9)^g

Open in a new tab

Abbreviations: NA, not applicable; OR, odds ratio; VA, US Department of Veterans Affairs.

^{^a}

Subset of the full sample with nonmissing values for credit scores during a 1-year look-back period (ie, those with index dates in 2016-2017 or in quarter 1 of 2015).

^{^b}

Minimally adjusted multivariable models controlled for age and race and ethnicity.

^{^c}

Fully adjusted multivariable models for veterans with VA health care use controlled for age; race and ethnicity; marital status; and prior depression, posttraumatic stress disorder, and substance use disorder.

^{^d}

Positive or negative 1-year credit score change of 0 to 9 points.

^{^e}

Positive 1-year credit score changes of 10 or more points.

^{^f}

Because score penalties are not uniform, these ranges are illustrative: 10- to 25-point reductions may be associated with a new credit inquiry; 26- to 50-point reductions with multiple minor late payments (≥30 days past due), credit utilization above approximately 50%, or closure of older accounts; 51- to 100-point reductions with late payments of 60 to 90 days, credit utilization approaching or exceeding limits (approximately 90% or greater), or a small account sent to collections; and reductions greater than 100 points with multiple collections, bankruptcy, repossession, or foreclosure.

^{^g}

Indicates statistical significance.

Discussion

This case-control analysis of linked financial, mortality, and health care data showed that financial stressors were associated with increased odds of suicide among veterans. Consistent with prior research,^9,10,11,17 cumulative financial stressors were associated with the largest effect sizes. To our knowledge, the current study is the first to identify associations between suicide and delinquent mortgage payments, repossessions, credit card delinquencies, and lower or decreasing credit scores. Among VA users, associations of late credit card payments and 3 or more financial stressors with suicide remained statistically significant in fully adjusted models that controlled for marital status and prior psychiatric diagnoses. Odds ratios were lower among VA users, suggesting a potential protective effect of VA use.

By identifying specific, verifiable financial risk factors, this study provides strong evidence linking suicide to financial hardship,^{5,6,7,9,10,11} particularly unsecured debt.²¹ These results align with theories of suicide^34,35,36 that emphasize perceived burdensomeness and hopelessness, which may be fueled by financial debt (eg, credit decline) or housing instability (eg, delinquent mortgage). Situating financial stressors within these frameworks highlights how economic realities may intersect with psychological processes.

The findings underscore the importance of addressing environmental-level factors (eg, debt) and individual-level factors (eg, psychiatric diagnoses) in suicide prevention.³⁷ Financial stress is a leading stressor in the US, often worsening with unemployment, rising housing costs, or economic volatility.^15,38,39 These findings suggest clinicians could inquire about specific financial stressors during suicide risk assessments. Safety planning could also connect patients at risk for suicide to financial education,^40,41 housing stabilization,^42,43 vocational rehabilitation,^15,43,44 and financial counseling.¹⁵ Intervention studies are needed to determine whether addressing financial stressors reduces suicide risk. Future research should also examine practical considerations for linking patients to financial services.

Monitoring financial stability over time may aid early detection of escalating suicide risk. Because financial stressors may evoke anxiety, shame, and distress,^45,46 clinicians should provide a nonjudgmental environment for patients to feel safe disclosing financial distress relevant to suicide risk. Ethical considerations discussing financial issues in mental health care warrant attention. For example, what are the consequences of identifying someone as potentially experiencing financial distress, and how might this affect the therapeutic alliance?

The association between credit score changes and suicide extends prior evidence linking debt,¹⁰ creditworthiness,⁴⁷ and suicide risk. Although credit score penalties vary, larger declines may reflect major adverse credit events, whereas moderate declines may reflect late payments or high credit utilization.^48,49,50 One-year credit change data were available for only a subset of the sample, which could introduce selection bias; however, consistency with other findings, similarity of this subset to the entire sample, and prior research together suggest that credit score change could be a useful indicator of suicide risk. Further research on this potential association is warranted.

Overall, our findings highlight roles for health care and financial services in suicide prevention. It is unknown how often individuals experiencing severe distress from mortgage delinquencies, repossessions, late credit card payments, or credit collapse share this financial information with health care professionals, suggesting value in screening for financial warning signs. Financial professionals or family members may connect individuals experiencing financial stressors to mental health services.

The link between financial stressors and suicide risk appeared weaker among VA users than non-VA users. Among VA users, positive credit score changes were associated with lower odds of suicide. This finding suggests that clinical care may be protective, potentially by addressing psychiatric needs or facilitating access to existing VA financial, housing, education, and employment supports. Current examples include the Veterans Benefits Banking Program (financial counseling), the National Veterans Financial Resource Center (financial literacy), VA Home Loans and Homeless Programs (housing), GI Bill (education), and Vocational Rehabilitation (employment). Future research should examine differences between veterans who use and do not use VA health care.

Strengths and Limitations

Strengths of this study included verified financial data to investigate specific stressors, a matched national sample, confirmed death by suicide and clinical data, and temporal ordering of financial stressors within the year before suicide. Study limitations should also be considered. First, although linked administrative and financial data enhance study rigor compared with self-report, they do not capture all domains, such as informal debt, income instability, or perceived financial stress. Also, inaccuracies in credit data may introduce information bias. Second, although financial stressors preceded suicide, the observational design precludes causal inference. Third, despite adjusting for clinical variables among veterans who use VA health care services, residual or unmeasured confounding (eg, interpersonal stressors or undiagnosed psychiatric symptoms) may remain. Fourth, diagnoses were operationalized as occurring prior to the year in which financial stressors were measured among VA users; however, temporal ordering remains complex, and prospective and experimental studies are needed given likely bidirectional influences.²¹ Fifth, because the sample included primarily male veterans, findings may not generalize broadly, although financial stressors identified may inform civilian suicide prevention. To enhance generalizability and reduce confounding, suicide decedents were randomly sampled regardless of VA status and matched to controls based on VA enrollment and use. Sixth, data from 2015 to 2017 may not reflect contemporary associations between financial stressors and suicide; newer data and analyses may be needed. Seventh, although most FinThrive indicators had minimal missing data, 1-year change in credit scores was only available for 12 228 suicide decedents and matched controls whose index dates occurred in 2016 and 2017 and in quarter 1 of 2015. Although bias cannot be ruled out, the subset of veterans with available 1-year change data was similar to the full sample across levels of demographic and key exposure variables, suggesting that those retained for the 1-year credit change analysis were representative of the entire sample.

Conclusions

In this case-control study, verified financial stressors—including mortgage delinquency, repossessions, past-due credit card payments, lower and declining credit scores, and cumulative numbers of stressors—were associated with increased odds of suicide. Integrating national credit data with confirmed suicide deaths enhanced precision and timing of financial risk measurement while accounting for key psychiatric diagnoses among VA users. These findings identify financial stressors as potential contributors to suicide among veterans and likely the general population. Incorporating strategies to identify and mitigate financial stressors may support comprehensive approaches to reducing suicide mortality.

Supplement 1.

eFigure. Flow Diagram Depicting Development of Analytic Sample

eTable. International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) and International Classification of Diseases and Other Related Health Problems, Tenth Revision, Clinical Modification (ICD-10-CM) Codes for Diagnoses of Depression, Posttraumatic Stress Disorder (PTSD), and Substance Use Disorder (SUD)

jamanetwopen-e2620981-s001.pdf^{(155.3KB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2620981-s002.pdf^{(15.4KB, pdf)}

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supplement 1.

eFigure. Flow Diagram Depicting Development of Analytic Sample

jamanetwopen-e2620981-s001.pdf^{(155.3KB, pdf)}

Supplement 2.

Data Sharing Statement

jamanetwopen-e2620981-s002.pdf^{(15.4KB, pdf)}

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PERMALINK

Financial Stressors and Suicide

Eric B Elbogen, PhD

Tess A Gilbert, MHS

Bryann B DeBeer, PhD

Elizabeth R Hooker, MS, MPH

Sarah Shull, PhD

Nathan A Kimbrel, PhD

Terri K Pogoda, PhD

Jason I Chen, PhD

Kathleen F Carlson, PhD

Key Points

Question

Findings

Meaning

Abstract

Importance

Objective

Design, Setting, and Participants

Exposures

Main Outcomes and Measures

Results

Conclusions and Relevance

Introduction

Methods

Study Design

Outcome and Exposures

Statistical Analysis

Results

Table 1. Characteristics of National Sample of Suicide Decedents and Matched Living Controls.

Table 2. Associations Between Financial Stressors and Subsequent Death by Suicide (N = 17 439).

Table 3. Associations Between Financial Stressors and Subsequent Death by Suicide Among Veterans With Veterans Affairs Health Care Use (N = 5177).

Table 4. Associations Between Financial Stressors and Subsequent Death by Suicide Among Veterans With No Veterans Affairs Health Care Use (N = 12 262).

Table 5. Associations Between 1-Year Change in Credit Score and Subsequent Death by Suicidea.

Discussion

Strengths and Limitations

Conclusions

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

Similar articles

Cited by other articles

Links to NCBI Databases

Table 5. Associations Between 1-Year Change in Credit Score and Subsequent Death by Suicide^a.